From 8548a87bd49c19bcda64cf0d6f205a378c1cebc3 Mon Sep 17 00:00:00 2001
From: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Date: Sun, 9 Feb 2025 14:24:01 +0100
Subject: [PATCH 1/3] Remove dataset tests artifacts (#701)

---
 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_mobile_cabinet/meta_data/info.json  | 11 ---
 .../meta_data/stats.safetensors               |  3 -
 .../train/data-00000-of-00001.arrow           |  3 -
 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_cabinet/train/state.json     | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_mobile_chair/meta_data/info.json    | 11 ---
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 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_chair/train/state.json       | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_mobile_elevator/meta_data/info.json | 11 ---
 .../meta_data/stats.safetensors               |  3 -
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 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_elevator/train/state.json    | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_mobile_shrimp/meta_data/info.json   | 11 ---
 .../meta_data/stats.safetensors               |  3 -
 .../train/data-00000-of-00001.arrow           |  3 -
 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_shrimp/train/state.json      | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_mobile_wash_pan/meta_data/info.json | 11 ---
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 .../train/data-00000-of-00001.arrow           |  3 -
 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_wash_pan/train/state.json    | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
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 .../train/dataset_info.json                   | 61 -----------------
 .../aloha_mobile_wipe_wine/train/state.json   | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
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 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
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 .../train/dataset_info.json                   | 64 ------------------
 .../aloha_static_coffee/train/state.json      | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
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 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
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 .../meta_data/episode_data_index.safetensors  |  3 -
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 .../train/dataset_info.json                   | 64 ------------------
 .../aloha_static_coffee_new/train/state.json  | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
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 .../train/dataset_info.json                   | 56 ----------------
 .../aloha_static_cups_open/train/state.json   | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
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 .../train/dataset_info.json                   | 64 ------------------
 .../aloha_static_pro_pencil/train/state.json  | 13 ----
 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
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 ....images.cam_right_wrist_episode_000000.mp4 |  3 -
 .../meta_data/episode_data_index.safetensors  |  3 -
 .../aloha_static_tape/meta_data/info.json     | 11 ---
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 ...rvation.images.cam_high_episode_000000.mp4 |  3 -
 ...n.images.cam_left_wrist_episode_000000.mp4 |  3 -
 ...ervation.images.cam_low_episode_000000.mp4 |  3 -
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 366 files changed, 4212 deletions(-)
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/meta_data/info.json
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/meta_data/stats.safetensors
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/train/data-00000-of-00001.arrow
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/train/dataset_info.json
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/train/state.json
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/videos/observation.images.cam_high_episode_000000.mp4
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/videos/observation.images.cam_left_wrist_episode_000000.mp4
 delete mode 100644 tests/data/lerobot/aloha_mobile_cabinet/videos/observation.images.cam_right_wrist_episode_000000.mp4
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/meta_data/episode_data_index.safetensors
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/meta_data/info.json
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/meta_data/stats.safetensors
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/train/data-00000-of-00001.arrow
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/train/dataset_info.json
 delete mode 100644 tests/data/lerobot/aloha_mobile_chair/train/state.json
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diff --git a/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors b/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors
deleted file mode 100644
index 2a11302f..00000000
--- a/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/tests/data/lerobot/aloha_mobile_cabinet/meta_data/info.json
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-{
-    "codebase_version": "v1.6",
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-version https://git-lfs.github.com/spec/v1
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index 76df9e36..00000000
--- a/tests/data/lerobot/aloha_mobile_cabinet/train/dataset_info.json
+++ /dev/null
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-{
-  "citation": "",
-  "description": "",
-  "features": {
-    "observation.images.cam_high": {
-      "_type": "VideoFrame"
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-    "observation.images.cam_left_wrist": {
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-        "dtype": "float32",
-        "_type": "Value"
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-      "_type": "Sequence"
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-    "episode_index": {
-      "dtype": "int64",
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-    "frame_index": {
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-  "license": ""
-}
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diff --git a/tests/data/lerobot/aloha_mobile_cabinet/train/state.json b/tests/data/lerobot/aloha_mobile_cabinet/train/state.json
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--- a/tests/data/lerobot/aloha_mobile_cabinet/train/state.json
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-{
-  "_data_files": [
-    {
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\ No newline at end of file
diff --git a/tests/data/lerobot/aloha_mobile_cabinet/videos/observation.images.cam_high_episode_000000.mp4 b/tests/data/lerobot/aloha_mobile_cabinet/videos/observation.images.cam_high_episode_000000.mp4
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-version https://git-lfs.github.com/spec/v1
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diff --git a/tests/data/lerobot/aloha_mobile_chair/meta_data/episode_data_index.safetensors b/tests/data/lerobot/aloha_mobile_chair/meta_data/episode_data_index.safetensors
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--- a/tests/data/lerobot/aloha_mobile_chair/meta_data/info.json
+++ /dev/null
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-{
-    "codebase_version": "v1.6",
-    "fps": 50,
-    "video": true,
-    "encoding": {
-        "vcodec": "libsvtav1",
-        "pix_fmt": "yuv420p",
-        "g": 2,
-        "crf": 30
-    }
-}
\ No newline at end of file
diff --git a/tests/data/lerobot/aloha_mobile_chair/meta_data/stats.safetensors b/tests/data/lerobot/aloha_mobile_chair/meta_data/stats.safetensors
deleted file mode 100644
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deleted file mode 100644
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-{
-  "citation": "",
-  "description": "",
-  "features": {
-    "observation.images.cam_high": {
-      "_type": "VideoFrame"
-    },
-    "observation.images.cam_left_wrist": {
-      "_type": "VideoFrame"
-    },
-    "observation.images.cam_right_wrist": {
-      "_type": "VideoFrame"
-    },
-    "observation.state": {
-      "feature": {
-        "dtype": "float32",
-        "_type": "Value"
-      },
-      "length": 14,
-      "_type": "Sequence"
-    },
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diff --git a/tests/data/lerobot/aloha_sim_transfer_cube_scripted_image/meta_data/stats.safetensors b/tests/data/lerobot/aloha_sim_transfer_cube_scripted_image/meta_data/stats.safetensors
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diff --git a/tests/data/lerobot/aloha_sim_transfer_cube_scripted_image/train/state.json b/tests/data/lerobot/aloha_sim_transfer_cube_scripted_image/train/state.json
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diff --git a/tests/data/lerobot/aloha_static_battery/meta_data/episode_data_index.safetensors b/tests/data/lerobot/aloha_static_battery/meta_data/episode_data_index.safetensors
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index 221370d7..00000000
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diff --git a/tests/data/lerobot/aloha_static_vinh_cup_left/meta_data/stats.safetensors b/tests/data/lerobot/aloha_static_vinh_cup_left/meta_data/stats.safetensors
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diff --git a/tests/data/lerobot/xarm_lift_medium_replay_image/train/state.json b/tests/data/lerobot/xarm_lift_medium_replay_image/train/state.json
deleted file mode 100644
index 408af7e8..00000000
--- a/tests/data/lerobot/xarm_lift_medium_replay_image/train/state.json
+++ /dev/null
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-{
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diff --git a/tests/data/lerobot/xarm_push_medium/meta_data/episode_data_index.safetensors b/tests/data/lerobot/xarm_push_medium/meta_data/episode_data_index.safetensors
deleted file mode 100644
index 3cf01178..00000000
--- a/tests/data/lerobot/xarm_push_medium/meta_data/episode_data_index.safetensors
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 6ea0c259..00000000
--- a/tests/data/lerobot/xarm_push_medium/meta_data/info.json
+++ /dev/null
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-{
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\ No newline at end of file
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deleted file mode 100644
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deleted file mode 100644
index e20086d4..00000000
--- a/tests/data/lerobot/xarm_push_medium/train/data-00000-of-00001.arrow
+++ /dev/null
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diff --git a/tests/data/lerobot/xarm_push_medium/train/dataset_info.json b/tests/data/lerobot/xarm_push_medium/train/dataset_info.json
deleted file mode 100644
index 69bf84eb..00000000
--- a/tests/data/lerobot/xarm_push_medium/train/dataset_info.json
+++ /dev/null
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-{
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\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium/train/state.json b/tests/data/lerobot/xarm_push_medium/train/state.json
deleted file mode 100644
index b792da32..00000000
--- a/tests/data/lerobot/xarm_push_medium/train/state.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "_data_files": [
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\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium/videos/observation.image_episode_000000.mp4 b/tests/data/lerobot/xarm_push_medium/videos/observation.image_episode_000000.mp4
deleted file mode 100644
index a8fa457e..00000000
--- a/tests/data/lerobot/xarm_push_medium/videos/observation.image_episode_000000.mp4
+++ /dev/null
@@ -1,3 +0,0 @@
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-size 18765
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deleted file mode 100644
index e7e90dc3..00000000
--- a/tests/data/lerobot/xarm_push_medium_image/meta_data/episode_data_index.safetensors
+++ /dev/null
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index 5f1c5d0d..00000000
--- a/tests/data/lerobot/xarm_push_medium_image/meta_data/info.json
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-{
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\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_image/meta_data/stats.safetensors b/tests/data/lerobot/xarm_push_medium_image/meta_data/stats.safetensors
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deleted file mode 100644
index 53cd0088..00000000
--- a/tests/data/lerobot/xarm_push_medium_image/train/data-00000-of-00001.arrow
+++ /dev/null
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deleted file mode 100644
index 9e47b345..00000000
--- a/tests/data/lerobot/xarm_push_medium_image/train/dataset_info.json
+++ /dev/null
@@ -1,51 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_image/train/state.json b/tests/data/lerobot/xarm_push_medium_image/train/state.json
deleted file mode 100644
index 408af7e8..00000000
--- a/tests/data/lerobot/xarm_push_medium_image/train/state.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
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diff --git a/tests/data/lerobot/xarm_push_medium_replay/meta_data/episode_data_index.safetensors b/tests/data/lerobot/xarm_push_medium_replay/meta_data/episode_data_index.safetensors
deleted file mode 100644
index 3cf01178..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/meta_data/episode_data_index.safetensors
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 6ea0c259..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/meta_data/info.json
+++ /dev/null
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-{
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diff --git a/tests/data/lerobot/xarm_push_medium_replay/meta_data/stats.safetensors b/tests/data/lerobot/xarm_push_medium_replay/meta_data/stats.safetensors
deleted file mode 100644
index 5c9a0247..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/meta_data/stats.safetensors
+++ /dev/null
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deleted file mode 100644
index c45994c6..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/train/data-00000-of-00001.arrow
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/tests/data/lerobot/xarm_push_medium_replay/train/dataset_info.json b/tests/data/lerobot/xarm_push_medium_replay/train/dataset_info.json
deleted file mode 100644
index 69bf84eb..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/train/dataset_info.json
+++ /dev/null
@@ -1,51 +0,0 @@
-{
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-  "description": "",
-  "features": {
-    "observation.image": {
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\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_replay/train/state.json b/tests/data/lerobot/xarm_push_medium_replay/train/state.json
deleted file mode 100644
index c22dad8a..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/train/state.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "_data_files": [
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-  "_fingerprint": "c610e979e9f89905",
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\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_replay/videos/observation.image_episode_000000.mp4 b/tests/data/lerobot/xarm_push_medium_replay/videos/observation.image_episode_000000.mp4
deleted file mode 100644
index 22ceddc7..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay/videos/observation.image_episode_000000.mp4
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/episode_data_index.safetensors b/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/episode_data_index.safetensors
deleted file mode 100644
index e7e90dc3..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/episode_data_index.safetensors
+++ /dev/null
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deleted file mode 100644
index 5f1c5d0d..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/info.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
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-    "fps": 15,
-    "video": 0
-}
\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/stats.safetensors b/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/stats.safetensors
deleted file mode 100644
index c5e2b21b..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/meta_data/stats.safetensors
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/tests/data/lerobot/xarm_push_medium_replay_image/train/data-00000-of-00001.arrow b/tests/data/lerobot/xarm_push_medium_replay_image/train/data-00000-of-00001.arrow
deleted file mode 100644
index 53cd0088..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/train/data-00000-of-00001.arrow
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/tests/data/lerobot/xarm_push_medium_replay_image/train/dataset_info.json b/tests/data/lerobot/xarm_push_medium_replay_image/train/dataset_info.json
deleted file mode 100644
index 9e47b345..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/train/dataset_info.json
+++ /dev/null
@@ -1,51 +0,0 @@
-{
-  "citation": "",
-  "description": "",
-  "features": {
-    "observation.image": {
-      "_type": "Image"
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-}
\ No newline at end of file
diff --git a/tests/data/lerobot/xarm_push_medium_replay_image/train/state.json b/tests/data/lerobot/xarm_push_medium_replay_image/train/state.json
deleted file mode 100644
index 408af7e8..00000000
--- a/tests/data/lerobot/xarm_push_medium_replay_image/train/state.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "data-00000-of-00001.arrow"
-    }
-  ],
-  "_fingerprint": "e70528672ed7674c",
-  "_format_columns": null,
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From 334deb985dc8f4822fd26712e76bb5ab47c5d01d Mon Sep 17 00:00:00 2001
From: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Date: Mon, 10 Feb 2025 17:22:15 +0100
Subject: [PATCH 2/3] Update CI trigger rules (#712)

---
 .github/workflows/quality.yml           | 2 --
 .github/workflows/test-docker-build.yml | 2 --
 .github/workflows/test.yml              | 2 --
 3 files changed, 6 deletions(-)

diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml
index c245345f..6acc901e 100644
--- a/.github/workflows/quality.yml
+++ b/.github/workflows/quality.yml
@@ -4,8 +4,6 @@ on:
   workflow_dispatch:
   workflow_call:
   pull_request:
-    branches:
-      - main
   push:
     branches:
       - main
diff --git a/.github/workflows/test-docker-build.yml b/.github/workflows/test-docker-build.yml
index 979897b0..08a3ab08 100644
--- a/.github/workflows/test-docker-build.yml
+++ b/.github/workflows/test-docker-build.yml
@@ -4,8 +4,6 @@ name: Test Dockerfiles
 
 on:
   pull_request:
-    branches:
-      - main
     paths:
       # Run only when DockerFile files are modified
       - "docker/**"
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 3a79c60f..bc567418 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -2,8 +2,6 @@ name: Tests
 
 on:
   pull_request:
-    branches:
-      - main
     paths:
       - "lerobot/**"
       - "tests/**"

From 90e099b39fb7e40ff87904dbed5685ffefd05777 Mon Sep 17 00:00:00 2001
From: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Date: Tue, 11 Feb 2025 10:36:06 +0100
Subject: [PATCH 3/3] Remove offline training, refactor `train.py` and
 logging/checkpointing (#670)

Co-authored-by: Remi <remi.cadene@huggingface.co>
---
 Makefile                                      |  39 +-
 examples/3_train_policy.py                    |   3 +-
 examples/4_train_policy_with_script.md        |  22 +-
 .../advanced/2_calculate_validation_loss.py   |   4 +-
 lerobot/common/constants.py                   |  11 +
 lerobot/common/logger.py                      | 240 ---------
 lerobot/common/optim/factory.py               |  23 +-
 lerobot/common/optim/optimizers.py            |  48 ++
 lerobot/common/optim/schedulers.py            |  31 ++
 lerobot/common/policies/act/modeling_act.py   |   8 +-
 .../policies/diffusion/modeling_diffusion.py  |   5 +-
 lerobot/common/policies/pretrained.py         |  13 +-
 .../common/policies/tdmpc/modeling_tdmpc.py   |   5 +-
 .../common/policies/vqbet/modeling_vqbet.py   |  12 +-
 lerobot/common/utils/io_utils.py              |  84 +++
 lerobot/common/utils/logging_utils.py         | 163 ++++++
 lerobot/common/utils/random_utils.py          | 191 +++++++
 lerobot/common/utils/train_utils.py           | 161 ++++++
 lerobot/common/utils/utils.py                 |  57 --
 lerobot/common/utils/wandb_utils.py           | 121 +++++
 lerobot/configs/train.py                      |  75 +--
 lerobot/scripts/eval.py                       |  11 +-
 lerobot/scripts/train.py                      | 488 ++++--------------
 poetry.lock                                   |  24 +-
 pyproject.toml                                |   4 +-
 tests/conftest.py                             |   1 +
 tests/fixtures/optimizers.py                  |  26 +
 .../save_image_transforms_to_safetensors.py   |   2 +-
 tests/scripts/save_policy_to_safetensors.py   |   8 +-
 tests/test_control_robot.py                   |  17 +-
 tests/test_datasets.py                        |   2 +-
 tests/test_image_transforms.py                |   2 +-
 tests/test_io_utils.py                        |  74 +++
 tests/test_logging_utils.py                   | 107 ++++
 tests/test_optimizers.py                      |  43 ++
 tests/test_policies.py                        |   2 +-
 tests/test_random_utils.py                    | 109 ++++
 tests/test_schedulers.py                      |  81 +++
 tests/test_train_utils.py                     |  84 +++
 tests/test_utils.py                           |  49 --
 40 files changed, 1515 insertions(+), 935 deletions(-)
 delete mode 100644 lerobot/common/logger.py
 create mode 100644 lerobot/common/utils/logging_utils.py
 create mode 100644 lerobot/common/utils/random_utils.py
 create mode 100644 lerobot/common/utils/train_utils.py
 create mode 100644 lerobot/common/utils/wandb_utils.py
 create mode 100644 tests/fixtures/optimizers.py
 create mode 100644 tests/test_io_utils.py
 create mode 100644 tests/test_logging_utils.py
 create mode 100644 tests/test_optimizers.py
 create mode 100644 tests/test_random_utils.py
 create mode 100644 tests/test_schedulers.py
 create mode 100644 tests/test_train_utils.py

diff --git a/Makefile b/Makefile
index c216e009..bc10141a 100644
--- a/Makefile
+++ b/Makefile
@@ -39,8 +39,8 @@ test-act-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=4 \
-		--online.steps=0 \
+		--steps=4 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \
@@ -76,8 +76,8 @@ test-diffusion-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=2 \
-		--online.steps=0 \
+		--steps=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -106,8 +106,8 @@ test-tdmpc-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=2 \
-		--online.steps=0 \
+		--steps=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -126,30 +126,3 @@ test-tdmpc-ete-eval:
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--device=$(DEVICE)
-
-# TODO(rcadene): fix online buffer to storing "task"
-# test-tdmpc-ete-train-with-online:
-# 	python lerobot/scripts/train.py \
-# 		--policy.type=tdmpc \
-# 		--env.type=pusht \
-# 		--env.obs_type=environment_state_agent_pos \
-# 		--env.episode_length=5 \
-# 		--dataset.repo_id=lerobot/pusht_keypoints \
-# 		--dataset.image_transforms.enable=true \
-# 		--dataset.episodes="[0]" \
-# 		--batch_size=2 \
-# 		--offline.steps=2 \
-# 		--online.steps=20 \
-# 		--online.rollout_n_episodes=2 \
-# 		--online.rollout_batch_size=2 \
-# 		--online.steps_between_rollouts=10 \
-# 		--online.buffer_capacity=1000 \
-# 		--online.env_seed=10000 \
-# 		--save_checkpoint=false \
-# 		--save_freq=10 \
-# 		--log_freq=1 \
-# 		--eval.use_async_envs=true \
-# 		--eval.n_episodes=1 \
-# 		--eval.batch_size=1 \
-# 		--device=$(DEVICE) \
-# 		--output_dir=tests/outputs/tdmpc_online/
diff --git a/examples/3_train_policy.py b/examples/3_train_policy.py
index 635c7293..cf5d4d3e 100644
--- a/examples/3_train_policy.py
+++ b/examples/3_train_policy.py
@@ -86,8 +86,7 @@ def main():
     while not done:
         for batch in dataloader:
             batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-            output_dict = policy.forward(batch)
-            loss = output_dict["loss"]
+            loss, _ = policy.forward(batch)
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
diff --git a/examples/4_train_policy_with_script.md b/examples/4_train_policy_with_script.md
index 9d57d424..58ed239a 100644
--- a/examples/4_train_policy_with_script.md
+++ b/examples/4_train_policy_with_script.md
@@ -161,13 +161,13 @@ python lerobot/scripts/train.py \
 ```
 You should see from the logging that your training picks up from where it left off.
 
-Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--offline.steps`, which is 100 000 by default.
+Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default.
 You could double the number of steps of the previous run with:
 ```bash
 python lerobot/scripts/train.py \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true \
-    --offline.steps=200000
+    --steps=200000
 ```
 
 ## Outputs of a run
@@ -175,12 +175,16 @@ In the output directory, there will be a folder called `checkpoints` with the fo
 ```bash
 outputs/train/run_resumption/checkpoints
 ├── 000100  # checkpoint_dir for training step 100
-│   ├── pretrained_model
-│   │   ├── config.json  # pretrained policy config
-│   │   ├── model.safetensors  # model weights
-│   │   ├── train_config.json  # train config
-│   │   └── README.md  # model card
-│   └── training_state.pth  # optimizer/scheduler/rng state and training step
+│   ├── pretrained_model/
+│   │   ├── config.json  # policy config
+│   │   ├── model.safetensors  # policy weights
+│   │   └── train_config.json  # train config
+│   └── training_state/
+│       ├── optimizer_param_groups.json  #  optimizer param groups
+│       ├── optimizer_state.safetensors  # optimizer state
+│       ├── rng_state.safetensors  # rng states
+│       ├── scheduler_state.json  # scheduler state
+│       └── training_step.json  # training step
 ├── 000200
 └── last -> 000200  # symlink to the last available checkpoint
 ```
@@ -250,7 +254,7 @@ python lerobot/scripts/train.py \
 python lerobot/scripts/train.py \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
-    --offline.steps=200000  # <- you can change some training parameters
+    --steps=200000  # <- you can change some training parameters
 ```
 
 #### Fine-tuning
diff --git a/examples/advanced/2_calculate_validation_loss.py b/examples/advanced/2_calculate_validation_loss.py
index 71e76072..6f234719 100644
--- a/examples/advanced/2_calculate_validation_loss.py
+++ b/examples/advanced/2_calculate_validation_loss.py
@@ -75,9 +75,9 @@ def main():
     n_examples_evaluated = 0
     for batch in val_dataloader:
         batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-        output_dict = policy.forward(batch)
+        loss, _ = policy.forward(batch)
 
-        loss_cumsum += output_dict["loss"].item()
+        loss_cumsum += loss.item()
         n_examples_evaluated += batch["index"].shape[0]
 
     # Calculate the average loss over the validation set.
diff --git a/lerobot/common/constants.py b/lerobot/common/constants.py
index 73889594..34da4ac0 100644
--- a/lerobot/common/constants.py
+++ b/lerobot/common/constants.py
@@ -4,3 +4,14 @@ OBS_ROBOT = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
+
+# files & directories
+CHECKPOINTS_DIR = "checkpoints"
+LAST_CHECKPOINT_LINK = "last"
+PRETRAINED_MODEL_DIR = "pretrained_model"
+TRAINING_STATE_DIR = "training_state"
+RNG_STATE = "rng_state.safetensors"
+TRAINING_STEP = "training_step.json"
+OPTIMIZER_STATE = "optimizer_state.safetensors"
+OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
+SCHEDULER_STATE = "scheduler_state.json"
diff --git a/lerobot/common/logger.py b/lerobot/common/logger.py
deleted file mode 100644
index 5f863f68..00000000
--- a/lerobot/common/logger.py
+++ /dev/null
@@ -1,240 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py
-
-# TODO(rcadene, alexander-soare): clean this file
-"""
-
-import logging
-import os
-import re
-from dataclasses import asdict
-from glob import glob
-from pathlib import Path
-
-import draccus
-import torch
-from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
-from termcolor import colored
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import LRScheduler
-
-from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.utils.utils import get_global_random_state
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.configs.types import FeatureType, NormalizationMode
-
-PRETRAINED_MODEL = "pretrained_model"
-TRAINING_STATE = "training_state.pth"
-
-
-def log_output_dir(out_dir):
-    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
-
-
-def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str:
-    """Return a group name for logging. Optionally returns group name as list."""
-    lst = [
-        f"policy:{cfg.policy.type}",
-        f"dataset:{cfg.dataset.repo_id}",
-        f"seed:{cfg.seed}",
-    ]
-    if cfg.env is not None:
-        lst.append(f"env:{cfg.env.type}")
-    return lst if return_list else "-".join(lst)
-
-
-def get_wandb_run_id_from_filesystem(checkpoint_dir: Path) -> str:
-    # Get the WandB run ID.
-    paths = glob(str(checkpoint_dir / "../wandb/latest-run/run-*"))
-    if len(paths) != 1:
-        raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
-    match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1])
-    if match is None:
-        raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
-    wandb_run_id = match.groups(0)[0]
-    return wandb_run_id
-
-
-class Logger:
-    """Primary logger object. Logs either locally or using wandb.
-
-    The logger creates the following directory structure:
-
-    provided_log_dir
-    ├── checkpoints
-    │   ├── specific_checkpoint_name
-    │   │   ├── pretrained_model  # Hugging Face pretrained model directory
-    │   │   │   ├── ...
-    │   │   └── training_state.pth  # optimizer, scheduler, and random states + training step
-    |   ├── another_specific_checkpoint_name
-    │   │   ├── ...
-    |   ├── ...
-    │   └── last  # a softlink to the last logged checkpoint
-    """
-
-    pretrained_model_dir_name = PRETRAINED_MODEL
-    training_state_file_name = TRAINING_STATE
-
-    def __init__(self, cfg: TrainPipelineConfig):
-        self._cfg = cfg
-        self.log_dir = cfg.output_dir
-        self.log_dir.mkdir(parents=True, exist_ok=True)
-        self.job_name = cfg.job_name
-        self.checkpoints_dir = self.get_checkpoints_dir(self.log_dir)
-        self.last_checkpoint_dir = self.get_last_checkpoint_dir(self.log_dir)
-        self.last_pretrained_model_dir = self.get_last_pretrained_model_dir(self.log_dir)
-
-        # Set up WandB.
-        self._group = cfg_to_group(cfg)
-        run_offline = not cfg.wandb.enable or not cfg.wandb.project
-        if run_offline:
-            logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
-            self._wandb = None
-        else:
-            os.environ["WANDB_SILENT"] = "true"
-            import wandb
-
-            wandb_run_id = None
-            if cfg.resume:
-                wandb_run_id = get_wandb_run_id_from_filesystem(self.checkpoints_dir)
-
-            wandb.init(
-                id=wandb_run_id,
-                project=cfg.wandb.project,
-                entity=cfg.wandb.entity,
-                name=self.job_name,
-                notes=cfg.wandb.notes,
-                tags=cfg_to_group(cfg, return_list=True),
-                dir=self.log_dir,
-                config=asdict(self._cfg),
-                # TODO(rcadene): try set to True
-                save_code=False,
-                # TODO(rcadene): split train and eval, and run async eval with job_type="eval"
-                job_type="train_eval",
-                resume="must" if cfg.resume else None,
-            )
-            print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
-            logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
-            self._wandb = wandb
-
-    @classmethod
-    def get_checkpoints_dir(cls, log_dir: str | Path) -> Path:
-        """Given the log directory, get the sub-directory in which checkpoints will be saved."""
-        return Path(log_dir) / "checkpoints"
-
-    @classmethod
-    def get_last_checkpoint_dir(cls, log_dir: str | Path) -> Path:
-        """Given the log directory, get the sub-directory in which the last checkpoint will be saved."""
-        return cls.get_checkpoints_dir(log_dir) / "last"
-
-    @classmethod
-    def get_last_pretrained_model_dir(cls, log_dir: str | Path) -> Path:
-        """
-        Given the log directory, get the sub-directory in which the last checkpoint's pretrained weights will
-        be saved.
-        """
-        return cls.get_last_checkpoint_dir(log_dir) / cls.pretrained_model_dir_name
-
-    def save_model(self, save_dir: Path, policy: PreTrainedPolicy, wandb_artifact_name: str | None = None):
-        """Save the weights of the Policy model using PyTorchModelHubMixin.
-
-        The weights are saved in a folder called "pretrained_model" under the checkpoint directory.
-
-        Optionally also upload the model to WandB.
-        """
-
-        self.checkpoints_dir.mkdir(parents=True, exist_ok=True)
-        register_features_types()
-        policy.save_pretrained(save_dir)
-        # Also save the full config for the env configuration.
-        self._cfg.save_pretrained(save_dir)
-        if self._wandb and not self._cfg.wandb.disable_artifact:
-            # note wandb artifact does not accept ":" or "/" in its name
-            artifact = self._wandb.Artifact(wandb_artifact_name, type="model")
-            artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
-            self._wandb.log_artifact(artifact)
-        if self.last_checkpoint_dir.exists():
-            os.remove(self.last_checkpoint_dir)
-
-    def save_training_state(
-        self,
-        save_dir: Path,
-        train_step: int,
-        optimizer: Optimizer | None = None,
-        scheduler: LRScheduler | None = None,
-    ):
-        """Checkpoint the global training_step, optimizer state, scheduler state, and random state.
-
-        All of these are saved as "training_state.pth" under the checkpoint directory.
-        """
-        training_state = {}
-        training_state["step"] = train_step
-        training_state.update(get_global_random_state())
-        if optimizer is not None:
-            training_state["optimizer"] = optimizer.state_dict()
-        if scheduler is not None:
-            training_state["scheduler"] = scheduler.state_dict()
-        torch.save(training_state, save_dir / self.training_state_file_name)
-
-    def save_checkpoint(
-        self,
-        train_step: int,
-        identifier: str,
-        policy: PreTrainedPolicy,
-        optimizer: Optimizer | None = None,
-        scheduler: LRScheduler | None = None,
-    ):
-        """Checkpoint the model weights and the training state."""
-        checkpoint_dir = self.checkpoints_dir / str(identifier)
-        wandb_artifact_name = (
-            None
-            if self._wandb is None
-            else f"{self._group.replace(':', '_').replace('/', '_')}-{self._cfg.seed}-{identifier}"
-        )
-        self.save_model(
-            checkpoint_dir / self.pretrained_model_dir_name, policy, wandb_artifact_name=wandb_artifact_name
-        )
-        self.save_training_state(checkpoint_dir, train_step, optimizer, scheduler)
-
-        relative_target = checkpoint_dir.relative_to(self.last_checkpoint_dir.parent)
-        self.last_checkpoint_dir.symlink_to(relative_target)
-
-    def log_dict(self, d: dict, step: int, mode: str = "train"):
-        assert mode in {"train", "eval"}
-        # TODO(alexander-soare): Add local text log.
-        if self._wandb is not None:
-            for k, v in d.items():
-                if not isinstance(v, (int, float, str)):
-                    logging.warning(
-                        f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
-                    )
-                    continue
-                self._wandb.log({f"{mode}/{k}": v}, step=step)
-
-    def log_video(self, video_path: str, step: int, mode: str = "train"):
-        assert mode in {"train", "eval"}
-        assert self._wandb is not None
-        wandb_video = self._wandb.Video(video_path, fps=self._cfg.env.fps, format="mp4")
-        self._wandb.log({f"{mode}/video": wandb_video}, step=step)
-
-
-def register_features_types():
-    draccus.decode.register(FeatureType, lambda x: FeatureType[x])
-    draccus.encode.register(FeatureType, lambda x: x.name)
-
-    draccus.decode.register(NormalizationMode, lambda x: NormalizationMode[x])
-    draccus.encode.register(NormalizationMode, lambda x: x.name)
diff --git a/lerobot/common/optim/factory.py b/lerobot/common/optim/factory.py
index 010cd461..10ff3df7 100644
--- a/lerobot/common/optim/factory.py
+++ b/lerobot/common/optim/factory.py
@@ -14,15 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from pathlib import Path
 
-import torch
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
-from lerobot.common.logger import TRAINING_STATE
 from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
 from lerobot.configs.train import TrainPipelineConfig
 
 
@@ -40,22 +36,5 @@ def make_optimizer_and_scheduler(
     """
     params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
     optimizer = cfg.optimizer.build(params)
-    lr_scheduler = cfg.scheduler.build(optimizer, cfg.offline.steps) if cfg.scheduler is not None else None
+    lr_scheduler = cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None
     return optimizer, lr_scheduler
-
-
-def load_training_state(checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None) -> int:
-    """
-    Given the checkpoint directory, load the optimizer state, scheduler state, and random state, and
-    return the global training step.
-    """
-    # TODO(aliberts): use safetensors instead as weights_only=False is unsafe
-    training_state = torch.load(checkpoint_dir / TRAINING_STATE, weights_only=False)
-    optimizer.load_state_dict(training_state["optimizer"])
-    if scheduler is not None:
-        scheduler.load_state_dict(training_state["scheduler"])
-    elif "scheduler" in training_state:
-        raise ValueError("The checkpoint contains a scheduler state_dict, but no LRScheduler was provided.")
-    # Small HACK to get the expected keys: use `get_global_random_state`.
-    set_global_random_state({k: training_state[k] for k in get_global_random_state()})
-    return training_state["step"], optimizer, scheduler
diff --git a/lerobot/common/optim/optimizers.py b/lerobot/common/optim/optimizers.py
index 737305ad..0cf4124c 100644
--- a/lerobot/common/optim/optimizers.py
+++ b/lerobot/common/optim/optimizers.py
@@ -1,8 +1,32 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import abc
 from dataclasses import asdict, dataclass
+from pathlib import Path
 
 import draccus
 import torch
+from safetensors.torch import load_file, save_file
+
+from lerobot.common.constants import (
+    OPTIMIZER_PARAM_GROUPS,
+    OPTIMIZER_STATE,
+)
+from lerobot.common.datasets.utils import flatten_dict, unflatten_dict, write_json
+from lerobot.common.utils.io_utils import deserialize_json_into_object
 
 
 @dataclass
@@ -68,3 +92,27 @@ class SGDConfig(OptimizerConfig):
         kwargs = asdict(self)
         kwargs.pop("grad_clip_norm")
         return torch.optim.SGD(params, **kwargs)
+
+
+def save_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
+    state = optimizer.state_dict()
+    param_groups = state.pop("param_groups")
+    flat_state = flatten_dict(state)
+    save_file(flat_state, save_dir / OPTIMIZER_STATE)
+    write_json(param_groups, save_dir / OPTIMIZER_PARAM_GROUPS)
+
+
+def load_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer:
+    current_state_dict = optimizer.state_dict()
+    flat_state = load_file(save_dir / OPTIMIZER_STATE)
+    state = unflatten_dict(flat_state)
+    loaded_state_dict = {"state": {int(k): v for k, v in state["state"].items()}}
+
+    if "param_groups" in current_state_dict:
+        param_groups = deserialize_json_into_object(
+            save_dir / OPTIMIZER_PARAM_GROUPS, current_state_dict["param_groups"]
+        )
+        loaded_state_dict["param_groups"] = param_groups
+
+    optimizer.load_state_dict(loaded_state_dict)
+    return optimizer
diff --git a/lerobot/common/optim/schedulers.py b/lerobot/common/optim/schedulers.py
index 80d83bdf..7e158394 100644
--- a/lerobot/common/optim/schedulers.py
+++ b/lerobot/common/optim/schedulers.py
@@ -1,11 +1,31 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import abc
 import math
 from dataclasses import asdict, dataclass
+from pathlib import Path
 
 import draccus
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
+from lerobot.common.constants import SCHEDULER_STATE
+from lerobot.common.datasets.utils import write_json
+from lerobot.common.utils.io_utils import deserialize_json_into_object
+
 
 @dataclass
 class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC):
@@ -89,3 +109,14 @@ class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
             return cosine_decay_schedule(current_step)
 
         return LambdaLR(optimizer, lr_lambda, -1)
+
+
+def save_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> None:
+    state_dict = scheduler.state_dict()
+    write_json(state_dict, save_dir / SCHEDULER_STATE)
+
+
+def load_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> LRScheduler:
+    state_dict = deserialize_json_into_object(save_dir / SCHEDULER_STATE, scheduler.state_dict())
+    scheduler.load_state_dict(state_dict)
+    return scheduler
diff --git a/lerobot/common/policies/act/modeling_act.py b/lerobot/common/policies/act/modeling_act.py
index 615f238f..9a1036c3 100644
--- a/lerobot/common/policies/act/modeling_act.py
+++ b/lerobot/common/policies/act/modeling_act.py
@@ -144,7 +144,7 @@ class ACTPolicy(PreTrainedPolicy):
             self._action_queue.extend(actions.transpose(0, 1))
         return self._action_queue.popleft()
 
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
@@ -169,11 +169,11 @@ class ACTPolicy(PreTrainedPolicy):
                 (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
             )
             loss_dict["kld_loss"] = mean_kld.item()
-            loss_dict["loss"] = l1_loss + mean_kld * self.config.kl_weight
+            loss = l1_loss + mean_kld * self.config.kl_weight
         else:
-            loss_dict["loss"] = l1_loss
+            loss = l1_loss
 
-        return loss_dict
+        return loss, loss_dict
 
 
 class ACTTemporalEnsembler:
diff --git a/lerobot/common/policies/diffusion/modeling_diffusion.py b/lerobot/common/policies/diffusion/modeling_diffusion.py
index 7147f550..9ecadcb0 100644
--- a/lerobot/common/policies/diffusion/modeling_diffusion.py
+++ b/lerobot/common/policies/diffusion/modeling_diffusion.py
@@ -143,7 +143,7 @@ class DiffusionPolicy(PreTrainedPolicy):
         action = self._queues["action"].popleft()
         return action
 
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, None]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
@@ -153,7 +153,8 @@ class DiffusionPolicy(PreTrainedPolicy):
             )
         batch = self.normalize_targets(batch)
         loss = self.diffusion.compute_loss(batch)
-        return {"loss": loss}
+        # no output_dict so returning None
+        return loss, None
 
 
 def _make_noise_scheduler(name: str, **kwargs: dict) -> DDPMScheduler | DDIMScheduler:
diff --git a/lerobot/common/policies/pretrained.py b/lerobot/common/policies/pretrained.py
index 84767594..1729dfb0 100644
--- a/lerobot/common/policies/pretrained.py
+++ b/lerobot/common/policies/pretrained.py
@@ -163,12 +163,17 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         """
         raise NotImplementedError
 
+    # TODO(aliberts, rcadene): split into 'forward' and 'compute_loss'?
     @abc.abstractmethod
-    def forward(self, batch: dict[str, Tensor]) -> dict:
-        """Run the batch through the model and compute the loss for training or validation.
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]:
+        """_summary_
 
-        Returns a dictionary with "loss" and potentially other information. Apart from "loss" which is a Tensor, all
-        other items should be logging-friendly, native Python types.
+        Args:
+            batch (dict[str, Tensor]): _description_
+
+        Returns:
+            tuple[Tensor, dict | None]: The loss and potentially other information. Apart from the loss which
+                is a Tensor, all other items should be logging-friendly, native Python types.
         """
         raise NotImplementedError
 
diff --git a/lerobot/common/policies/tdmpc/modeling_tdmpc.py b/lerobot/common/policies/tdmpc/modeling_tdmpc.py
index 6366a5a4..c4f90b8d 100644
--- a/lerobot/common/policies/tdmpc/modeling_tdmpc.py
+++ b/lerobot/common/policies/tdmpc/modeling_tdmpc.py
@@ -302,7 +302,7 @@ class TDMPCPolicy(PreTrainedPolicy):
             G -= running_discount * self.config.uncertainty_regularizer_coeff * terminal_values.std(0)
         return G
 
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]:
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss.
 
         Returns a dictionary with loss as a tensor, and other information as native floats.
@@ -495,7 +495,6 @@ class TDMPCPolicy(PreTrainedPolicy):
                 "Q_value_loss": q_value_loss.item(),
                 "V_value_loss": v_value_loss.item(),
                 "pi_loss": pi_loss.item(),
-                "loss": loss,
                 "sum_loss": loss.item() * self.config.horizon,
             }
         )
@@ -505,7 +504,7 @@ class TDMPCPolicy(PreTrainedPolicy):
             if isinstance(batch[key], torch.Tensor) and batch[key].ndim > 1:
                 batch[key] = batch[key].transpose(1, 0)
 
-        return info
+        return loss, info
 
     def update(self):
         """Update the target model's parameters with an EMA step."""
diff --git a/lerobot/common/policies/vqbet/modeling_vqbet.py b/lerobot/common/policies/vqbet/modeling_vqbet.py
index c4d4a46d..1f70b186 100644
--- a/lerobot/common/policies/vqbet/modeling_vqbet.py
+++ b/lerobot/common/policies/vqbet/modeling_vqbet.py
@@ -156,7 +156,7 @@ class VQBeTPolicy(PreTrainedPolicy):
         action = self._queues["action"].popleft()
         return action
 
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
         batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
@@ -170,16 +170,16 @@ class VQBeTPolicy(PreTrainedPolicy):
             loss, n_different_codes, n_different_combinations, recon_l1_error = (
                 self.vqbet.action_head.discretize(self.config.n_vqvae_training_steps, batch["action"])
             )
-            return {
-                "loss": loss,
+            return loss, {
                 "n_different_codes": n_different_codes,
                 "n_different_combinations": n_different_combinations,
                 "recon_l1_error": recon_l1_error,
             }
         # if Residual VQ is already trained, VQ-BeT trains its GPT and bin prediction head / offset prediction head parts.
         _, loss_dict = self.vqbet(batch, rollout=False)
+        loss = loss_dict.pop("loss")
 
-        return loss_dict
+        return loss, loss_dict
 
 
 class SpatialSoftmax(nn.Module):
@@ -342,7 +342,7 @@ class VQBeTModel(nn.Module):
             torch.row_stack([torch.arange(i, i + self.config.action_chunk_size) for i in range(num_tokens)]),
         )
 
-    def forward(self, batch: dict[str, Tensor], rollout: bool) -> Tensor:
+    def forward(self, batch: dict[str, Tensor], rollout: bool) -> tuple[dict, dict]:
         # Input validation.
         assert set(batch).issuperset({"observation.state", "observation.images"})
         batch_size, n_obs_steps = batch["observation.state"].shape[:2]
@@ -482,7 +482,7 @@ class VQBeTHead(nn.Module):
                 param.requires_grad = False
         return loss, n_different_codes, n_different_combinations, recon_l1_error
 
-    def forward(self, x, **kwargs):
+    def forward(self, x, **kwargs) -> dict:
         # N is the batch size, and T is number of action query tokens, which are process through same GPT
         N, T, _ = x.shape
         # we calculate N and T side parallely. Thus, the dimensions would be
diff --git a/lerobot/common/utils/io_utils.py b/lerobot/common/utils/io_utils.py
index b85f17c7..3fc405f7 100644
--- a/lerobot/common/utils/io_utils.py
+++ b/lerobot/common/utils/io_utils.py
@@ -13,10 +13,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import json
 import warnings
+from pathlib import Path
+from typing import TypeVar
 
 import imageio
 
+JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...]
+T = TypeVar("T", bound=JsonLike)
+
 
 def write_video(video_path, stacked_frames, fps):
     # Filter out DeprecationWarnings raised from pkg_resources
@@ -25,3 +31,81 @@ def write_video(video_path, stacked_frames, fps):
             "ignore", "pkg_resources is deprecated as an API", category=DeprecationWarning
         )
         imageio.mimsave(video_path, stacked_frames, fps=fps)
+
+
+def deserialize_json_into_object(fpath: Path, obj: T) -> T:
+    """
+    Loads the JSON data from `fpath` and recursively fills `obj` with the
+    corresponding values (strictly matching structure and types).
+    Tuples in `obj` are expected to be lists in the JSON data, which will be
+    converted back into tuples.
+    """
+    with open(fpath, encoding="utf-8") as f:
+        data = json.load(f)
+
+    def _deserialize(target, source):
+        """
+        Recursively overwrite the structure in `target` with data from `source`,
+        performing strict checks on structure and type.
+        Returns the updated version of `target` (especially important for tuples).
+        """
+
+        # If the target is a dictionary, source must be a dictionary as well.
+        if isinstance(target, dict):
+            if not isinstance(source, dict):
+                raise TypeError(f"Type mismatch: expected dict, got {type(source)}")
+
+            # Check that they have exactly the same set of keys.
+            if target.keys() != source.keys():
+                raise ValueError(
+                    f"Dictionary keys do not match.\n" f"Expected: {target.keys()}, got: {source.keys()}"
+                )
+
+            # Recursively update each key.
+            for k in target:
+                target[k] = _deserialize(target[k], source[k])
+
+            return target
+
+        # If the target is a list, source must be a list as well.
+        elif isinstance(target, list):
+            if not isinstance(source, list):
+                raise TypeError(f"Type mismatch: expected list, got {type(source)}")
+
+            # Check length
+            if len(target) != len(source):
+                raise ValueError(f"List length mismatch: expected {len(target)}, got {len(source)}")
+
+            # Recursively update each element.
+            for i in range(len(target)):
+                target[i] = _deserialize(target[i], source[i])
+
+            return target
+
+        # If the target is a tuple, the source must be a list in JSON,
+        # which we'll convert back to a tuple.
+        elif isinstance(target, tuple):
+            if not isinstance(source, list):
+                raise TypeError(f"Type mismatch: expected list (for tuple), got {type(source)}")
+
+            if len(target) != len(source):
+                raise ValueError(f"Tuple length mismatch: expected {len(target)}, got {len(source)}")
+
+            # Convert each element, forming a new tuple.
+            converted_items = []
+            for t_item, s_item in zip(target, source, strict=False):
+                converted_items.append(_deserialize(t_item, s_item))
+
+            # Return a brand new tuple (tuples are immutable in Python).
+            return tuple(converted_items)
+
+        # Otherwise, we're dealing with a "primitive" (int, float, str, bool, None).
+        else:
+            # Check the exact type.  If these must match 1:1, do:
+            if type(target) is not type(source):
+                raise TypeError(f"Type mismatch: expected {type(target)}, got {type(source)}")
+            return source
+
+    # Perform the in-place/recursive deserialization
+    updated_obj = _deserialize(obj, data)
+    return updated_obj
diff --git a/lerobot/common/utils/logging_utils.py b/lerobot/common/utils/logging_utils.py
new file mode 100644
index 00000000..b99c348f
--- /dev/null
+++ b/lerobot/common/utils/logging_utils.py
@@ -0,0 +1,163 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any
+
+from lerobot.common.utils.utils import format_big_number
+
+
+class AverageMeter:
+    """
+    Computes and stores the average and current value
+    Adapted from https://github.com/pytorch/examples/blob/main/imagenet/main.py
+    """
+
+    def __init__(self, name: str, fmt: str = ":f"):
+        self.name = name
+        self.fmt = fmt
+        self.reset()
+
+    def reset(self) -> None:
+        self.val = 0.0
+        self.avg = 0.0
+        self.sum = 0.0
+        self.count = 0.0
+
+    def update(self, val: float, n: int = 1) -> None:
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __str__(self):
+        fmtstr = "{name}:{avg" + self.fmt + "}"
+        return fmtstr.format(**self.__dict__)
+
+
+class MetricsTracker:
+    """
+    A helper class to track and log metrics over time.
+
+    Usage pattern:
+
+    ```python
+    # initialize, potentially with non-zero initial step (e.g. if resuming run)
+    metrics = {"loss": AverageMeter("loss", ":.3f")}
+    train_metrics = MetricsTracker(cfg, dataset, metrics, initial_step=step)
+
+    # update metrics derived from step (samples, episodes, epochs) at each training step
+    train_metrics.step()
+
+    # update various metrics
+    loss = policy.forward(batch)
+    train_metrics.loss = loss
+
+    # display current metrics
+    logging.info(train_metrics)
+
+    # export for wandb
+    wandb.log(train_metrics.to_dict())
+
+    # reset averages after logging
+    train_metrics.reset_averages()
+    ```
+    """
+
+    __keys__ = [
+        "_batch_size",
+        "_num_frames",
+        "_avg_samples_per_ep",
+        "metrics",
+        "steps",
+        "samples",
+        "episodes",
+        "epochs",
+    ]
+
+    def __init__(
+        self,
+        batch_size: int,
+        num_frames: int,
+        num_episodes: int,
+        metrics: dict[str, AverageMeter],
+        initial_step: int = 0,
+    ):
+        self.__dict__.update({k: None for k in self.__keys__})
+        self._batch_size = batch_size
+        self._num_frames = num_frames
+        self._avg_samples_per_ep = num_frames / num_episodes
+        self.metrics = metrics
+
+        self.steps = initial_step
+        # A sample is an (observation,action) pair, where observation and action
+        # can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
+        self.samples = self.steps * self._batch_size
+        self.episodes = self.samples / self._avg_samples_per_ep
+        self.epochs = self.samples / self._num_frames
+
+    def __getattr__(self, name: str) -> int | dict[str, AverageMeter] | AverageMeter | Any:
+        if name in self.__dict__:
+            return self.__dict__[name]
+        elif name in self.metrics:
+            return self.metrics[name]
+        else:
+            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        if name in self.__dict__:
+            super().__setattr__(name, value)
+        elif name in self.metrics:
+            self.metrics[name].update(value)
+        else:
+            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
+
+    def step(self) -> None:
+        """
+        Updates metrics that depend on 'step' for one step.
+        """
+        self.steps += 1
+        self.samples += self._batch_size
+        self.episodes = self.samples / self._avg_samples_per_ep
+        self.epochs = self.samples / self._num_frames
+
+    def __str__(self) -> str:
+        display_list = [
+            f"step:{format_big_number(self.steps)}",
+            # number of samples seen during training
+            f"smpl:{format_big_number(self.samples)}",
+            # number of episodes seen during training
+            f"ep:{format_big_number(self.episodes)}",
+            # number of time all unique samples are seen
+            f"epch:{self.epochs:.2f}",
+            *[str(m) for m in self.metrics.values()],
+        ]
+        return " ".join(display_list)
+
+    def to_dict(self, use_avg: bool = True) -> dict[str, int | float]:
+        """
+        Returns the current metric values (or averages if `use_avg=True`) as a dict.
+        """
+        return {
+            "steps": self.steps,
+            "samples": self.samples,
+            "episodes": self.episodes,
+            "epochs": self.epochs,
+            **{k: m.avg if use_avg else m.val for k, m in self.metrics.items()},
+        }
+
+    def reset_averages(self) -> None:
+        """Resets average meters."""
+        for m in self.metrics.values():
+            m.reset()
diff --git a/lerobot/common/utils/random_utils.py b/lerobot/common/utils/random_utils.py
new file mode 100644
index 00000000..3d9bf4dd
--- /dev/null
+++ b/lerobot/common/utils/random_utils.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import random
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any, Generator
+
+import numpy as np
+import torch
+from safetensors.torch import load_file, save_file
+
+from lerobot.common.constants import RNG_STATE
+from lerobot.common.datasets.utils import flatten_dict, unflatten_dict
+
+
+def serialize_python_rng_state() -> dict[str, torch.Tensor]:
+    """
+    Returns the rng state for `random` in the form of a flat dict[str, torch.Tensor] to be saved using
+    `safetensors.save_file()` or `torch.save()`.
+    """
+    py_state = random.getstate()
+    return {
+        "py_rng_version": torch.tensor([py_state[0]], dtype=torch.int64),
+        "py_rng_state": torch.tensor(py_state[1], dtype=torch.int64),
+    }
+
+
+def deserialize_python_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None:
+    """
+    Restores the rng state for `random` from a dictionary produced by `serialize_python_rng_state()`.
+    """
+    py_state = (rng_state_dict["py_rng_version"].item(), tuple(rng_state_dict["py_rng_state"].tolist()), None)
+    random.setstate(py_state)
+
+
+def serialize_numpy_rng_state() -> dict[str, torch.Tensor]:
+    """
+    Returns the rng state for `numpy` in the form of a flat dict[str, torch.Tensor] to be saved using
+    `safetensors.save_file()` or `torch.save()`.
+    """
+    np_state = np.random.get_state()
+    # Ensure no breaking changes from numpy
+    assert np_state[0] == "MT19937"
+    return {
+        "np_rng_state_values": torch.tensor(np_state[1], dtype=torch.int64),
+        "np_rng_state_index": torch.tensor([np_state[2]], dtype=torch.int64),
+        "np_rng_has_gauss": torch.tensor([np_state[3]], dtype=torch.int64),
+        "np_rng_cached_gaussian": torch.tensor([np_state[4]], dtype=torch.float32),
+    }
+
+
+def deserialize_numpy_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None:
+    """
+    Restores the rng state for `numpy` from a dictionary produced by `serialize_numpy_rng_state()`.
+    """
+    np_state = (
+        "MT19937",
+        rng_state_dict["np_rng_state_values"].numpy(),
+        rng_state_dict["np_rng_state_index"].item(),
+        rng_state_dict["np_rng_has_gauss"].item(),
+        rng_state_dict["np_rng_cached_gaussian"].item(),
+    )
+    np.random.set_state(np_state)
+
+
+def serialize_torch_rng_state() -> dict[str, torch.Tensor]:
+    """
+    Returns the rng state for `torch` in the form of a flat dict[str, torch.Tensor] to be saved using
+    `safetensors.save_file()` or `torch.save()`.
+    """
+    torch_rng_state_dict = {"torch_rng_state": torch.get_rng_state()}
+    if torch.cuda.is_available():
+        torch_rng_state_dict["torch_cuda_rng_state"] = torch.cuda.get_rng_state()
+    return torch_rng_state_dict
+
+
+def deserialize_torch_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None:
+    """
+    Restores the rng state for `torch` from a dictionary produced by `serialize_torch_rng_state()`.
+    """
+    torch.set_rng_state(rng_state_dict["torch_rng_state"])
+    if torch.cuda.is_available() and "torch_cuda_rng_state" in rng_state_dict:
+        torch.cuda.set_rng_state(rng_state_dict["torch_cuda_rng_state"])
+
+
+def serialize_rng_state() -> dict[str, torch.Tensor]:
+    """
+    Returns the rng state for `random`, `numpy`, and `torch`, in the form of a flat
+    dict[str, torch.Tensor] to be saved using `safetensors.save_file()` `torch.save()`.
+    """
+    py_rng_state_dict = serialize_python_rng_state()
+    np_rng_state_dict = serialize_numpy_rng_state()
+    torch_rng_state_dict = serialize_torch_rng_state()
+
+    return {
+        **py_rng_state_dict,
+        **np_rng_state_dict,
+        **torch_rng_state_dict,
+    }
+
+
+def deserialize_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None:
+    """
+    Restores the rng state for `random`, `numpy`, and `torch` from a dictionary produced by
+    `serialize_rng_state()`.
+    """
+    py_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("py")}
+    np_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("np")}
+    torch_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("torch")}
+
+    deserialize_python_rng_state(py_rng_state_dict)
+    deserialize_numpy_rng_state(np_rng_state_dict)
+    deserialize_torch_rng_state(torch_rng_state_dict)
+
+
+def save_rng_state(save_dir: Path) -> None:
+    rng_state_dict = serialize_rng_state()
+    flat_rng_state_dict = flatten_dict(rng_state_dict)
+    save_file(flat_rng_state_dict, save_dir / RNG_STATE)
+
+
+def load_rng_state(save_dir: Path) -> None:
+    flat_rng_state_dict = load_file(save_dir / RNG_STATE)
+    rng_state_dict = unflatten_dict(flat_rng_state_dict)
+    deserialize_rng_state(rng_state_dict)
+
+
+def get_rng_state() -> dict[str, Any]:
+    """Get the random state for `random`, `numpy`, and `torch`."""
+    random_state_dict = {
+        "random_state": random.getstate(),
+        "numpy_random_state": np.random.get_state(),
+        "torch_random_state": torch.random.get_rng_state(),
+    }
+    if torch.cuda.is_available():
+        random_state_dict["torch_cuda_random_state"] = torch.cuda.random.get_rng_state()
+    return random_state_dict
+
+
+def set_rng_state(random_state_dict: dict[str, Any]):
+    """Set the random state for `random`, `numpy`, and `torch`.
+
+    Args:
+        random_state_dict: A dictionary of the form returned by `get_rng_state`.
+    """
+    random.setstate(random_state_dict["random_state"])
+    np.random.set_state(random_state_dict["numpy_random_state"])
+    torch.random.set_rng_state(random_state_dict["torch_random_state"])
+    if torch.cuda.is_available():
+        torch.cuda.random.set_rng_state(random_state_dict["torch_cuda_random_state"])
+
+
+def set_seed(seed) -> None:
+    """Set seed for reproducibility."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+@contextmanager
+def seeded_context(seed: int) -> Generator[None, None, None]:
+    """Set the seed when entering a context, and restore the prior random state at exit.
+
+    Example usage:
+
+    ```
+    a = random.random()  # produces some random number
+    with seeded_context(1337):
+        b = random.random()  # produces some other random number
+    c = random.random()  # produces yet another random number, but the same it would have if we never made `b`
+    ```
+    """
+    random_state_dict = get_rng_state()
+    set_seed(seed)
+    yield None
+    set_rng_state(random_state_dict)
diff --git a/lerobot/common/utils/train_utils.py b/lerobot/common/utils/train_utils.py
new file mode 100644
index 00000000..a7998312
--- /dev/null
+++ b/lerobot/common/utils/train_utils.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+from pathlib import Path
+
+from termcolor import colored
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+
+from lerobot.common.constants import (
+    CHECKPOINTS_DIR,
+    LAST_CHECKPOINT_LINK,
+    PRETRAINED_MODEL_DIR,
+    TRAINING_STATE_DIR,
+    TRAINING_STEP,
+)
+from lerobot.common.datasets.utils import load_json, write_json
+from lerobot.common.optim.optimizers import load_optimizer_state, save_optimizer_state
+from lerobot.common.optim.schedulers import load_scheduler_state, save_scheduler_state
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.utils.random_utils import load_rng_state, save_rng_state
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def log_output_dir(out_dir):
+    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
+
+
+def get_step_identifier(step: int, total_steps: int) -> str:
+    num_digits = max(6, len(str(total_steps)))
+    return f"{step:0{num_digits}d}"
+
+
+def get_step_checkpoint_dir(output_dir: Path, total_steps: int, step: int) -> Path:
+    """Returns the checkpoint sub-directory corresponding to the step number."""
+    step_identifier = get_step_identifier(step, total_steps)
+    return output_dir / CHECKPOINTS_DIR / step_identifier
+
+
+def save_training_step(step: int, save_dir: Path) -> None:
+    write_json({"step": step}, save_dir / TRAINING_STEP)
+
+
+def load_training_step(save_dir: Path) -> int:
+    training_step = load_json(save_dir / TRAINING_STEP)
+    return training_step["step"]
+
+
+def update_last_checkpoint(checkpoint_dir: Path) -> Path:
+    last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK
+    if last_checkpoint_dir.is_symlink():
+        last_checkpoint_dir.unlink()
+    relative_target = checkpoint_dir.relative_to(checkpoint_dir.parent)
+    last_checkpoint_dir.symlink_to(relative_target)
+
+
+def save_checkpoint(
+    checkpoint_dir: Path,
+    step: int,
+    cfg: TrainPipelineConfig,
+    policy: PreTrainedPolicy,
+    optimizer: Optimizer,
+    scheduler: LRScheduler | None = None,
+) -> None:
+    """This function creates the following directory structure:
+
+    005000/  #  training step at checkpoint
+    ├── pretrained_model/
+    │   ├── config.json  # policy config
+    │   ├── model.safetensors  # policy weights
+    │   └── train_config.json  # train config
+    └── training_state/
+        ├── optimizer_param_groups.json  #  optimizer param groups
+        ├── optimizer_state.safetensors  # optimizer state
+        ├── rng_state.safetensors  # rng states
+        ├── scheduler_state.json  # scheduler state
+        └── training_step.json  # training step
+
+    Args:
+        cfg (TrainPipelineConfig): The training config used for this run.
+        step (int): The training step at that checkpoint.
+        policy (PreTrainedPolicy): The policy to save.
+        optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None.
+        scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
+    """
+    pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
+    policy.save_pretrained(pretrained_dir)
+    cfg.save_pretrained(pretrained_dir)
+    save_training_state(checkpoint_dir, step, optimizer, scheduler)
+
+
+def save_training_state(
+    checkpoint_dir: Path,
+    train_step: int,
+    optimizer: Optimizer | None = None,
+    scheduler: LRScheduler | None = None,
+) -> None:
+    """
+    Saves the training step, optimizer state, scheduler state, and rng state.
+
+    Args:
+        save_dir (Path): The directory to save artifacts to.
+        train_step (int): Current training step.
+        optimizer (Optimizer | None, optional): The optimizer from which to save the state_dict.
+            Defaults to None.
+        scheduler (LRScheduler | None, optional): The scheduler from which to save the state_dict.
+            Defaults to None.
+    """
+    save_dir = checkpoint_dir / TRAINING_STATE_DIR
+    save_dir.mkdir(parents=True, exist_ok=True)
+    save_training_step(train_step, save_dir)
+    save_rng_state(save_dir)
+    if optimizer is not None:
+        save_optimizer_state(optimizer, save_dir)
+    if scheduler is not None:
+        save_scheduler_state(scheduler, save_dir)
+
+
+def load_training_state(
+    checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None
+) -> tuple[int, Optimizer, LRScheduler | None]:
+    """
+    Loads the training step, optimizer state, scheduler state, and rng state.
+    This is used to resume a training run.
+
+    Args:
+        checkpoint_dir (Path): The checkpoint directory. Should contain a 'training_state' dir.
+        optimizer (Optimizer): The optimizer to load the state_dict to.
+        scheduler (LRScheduler | None): The scheduler to load the state_dict to (can be None).
+
+    Raises:
+        NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir
+
+    Returns:
+        tuple[int, Optimizer, LRScheduler | None]: training step, optimizer and scheduler with their
+            state_dict loaded.
+    """
+    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
+    if not training_state_dir.is_dir():
+        raise NotADirectoryError(training_state_dir)
+
+    load_rng_state(training_state_dir)
+    step = load_training_step(training_state_dir)
+    optimizer = load_optimizer_state(optimizer, training_state_dir)
+    if scheduler is not None:
+        scheduler = load_scheduler_state(scheduler, training_state_dir)
+
+    return step, optimizer, scheduler
diff --git a/lerobot/common/utils/utils.py b/lerobot/common/utils/utils.py
index cb4f1874..015d1ede 100644
--- a/lerobot/common/utils/utils.py
+++ b/lerobot/common/utils/utils.py
@@ -17,14 +17,10 @@ import logging
 import os
 import os.path as osp
 import platform
-import random
-from contextlib import contextmanager
 from copy import copy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Generator
 
-import numpy as np
 import torch
 
 
@@ -106,59 +102,6 @@ def is_amp_available(device: str):
         raise ValueError(f"Unknown device '{device}.")
 
 
-def get_global_random_state() -> dict[str, Any]:
-    """Get the random state for `random`, `numpy`, and `torch`."""
-    random_state_dict = {
-        "random_state": random.getstate(),
-        "numpy_random_state": np.random.get_state(),
-        "torch_random_state": torch.random.get_rng_state(),
-    }
-    if torch.cuda.is_available():
-        random_state_dict["torch_cuda_random_state"] = torch.cuda.random.get_rng_state()
-    return random_state_dict
-
-
-def set_global_random_state(random_state_dict: dict[str, Any]):
-    """Set the random state for `random`, `numpy`, and `torch`.
-
-    Args:
-        random_state_dict: A dictionary of the form returned by `get_global_random_state`.
-    """
-    random.setstate(random_state_dict["random_state"])
-    np.random.set_state(random_state_dict["numpy_random_state"])
-    torch.random.set_rng_state(random_state_dict["torch_random_state"])
-    if torch.cuda.is_available():
-        torch.cuda.random.set_rng_state(random_state_dict["torch_cuda_random_state"])
-
-
-def set_global_seed(seed):
-    """Set seed for reproducibility."""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-
-@contextmanager
-def seeded_context(seed: int) -> Generator[None, None, None]:
-    """Set the seed when entering a context, and restore the prior random state at exit.
-
-    Example usage:
-
-    ```
-    a = random.random()  # produces some random number
-    with seeded_context(1337):
-        b = random.random()  # produces some other random number
-    c = random.random()  # produces yet another random number, but the same it would have if we never made `b`
-    ```
-    """
-    random_state_dict = get_global_random_state()
-    set_global_seed(seed)
-    yield None
-    set_global_random_state(random_state_dict)
-
-
 def init_logging():
     def custom_format(record):
         dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
diff --git a/lerobot/common/utils/wandb_utils.py b/lerobot/common/utils/wandb_utils.py
new file mode 100644
index 00000000..2ab3c3fd
--- /dev/null
+++ b/lerobot/common/utils/wandb_utils.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+import os
+import re
+from glob import glob
+from pathlib import Path
+
+from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
+from termcolor import colored
+
+from lerobot.common.constants import PRETRAINED_MODEL_DIR
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str:
+    """Return a group name for logging. Optionally returns group name as list."""
+    lst = [
+        f"policy:{cfg.policy.type}",
+        f"dataset:{cfg.dataset.repo_id}",
+        f"seed:{cfg.seed}",
+    ]
+    if cfg.env is not None:
+        lst.append(f"env:{cfg.env.type}")
+    return lst if return_list else "-".join(lst)
+
+
+def get_wandb_run_id_from_filesystem(log_dir: Path) -> str:
+    # Get the WandB run ID.
+    paths = glob(str(log_dir / "wandb/latest-run/run-*"))
+    if len(paths) != 1:
+        raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
+    match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1])
+    if match is None:
+        raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
+    wandb_run_id = match.groups(0)[0]
+    return wandb_run_id
+
+
+def get_safe_wandb_artifact_name(name: str):
+    """WandB artifacts don't accept ":" or "/" in their name."""
+    return name.replace(":", "_").replace("/", "_")
+
+
+class WandBLogger:
+    """A helper class to log object using wandb."""
+
+    def __init__(self, cfg: TrainPipelineConfig):
+        self.cfg = cfg.wandb
+        self.log_dir = cfg.output_dir
+        self.job_name = cfg.job_name
+        self.env_fps = cfg.env.fps if cfg.env else None
+        self._group = cfg_to_group(cfg)
+
+        # Set up WandB.
+        os.environ["WANDB_SILENT"] = "True"
+        import wandb
+
+        wandb_run_id = get_wandb_run_id_from_filesystem(self.log_dir) if cfg.resume else None
+        wandb.init(
+            id=wandb_run_id,
+            project=self.cfg.project,
+            entity=self.cfg.entity,
+            name=self.job_name,
+            notes=self.cfg.notes,
+            tags=cfg_to_group(cfg, return_list=True),
+            dir=self.log_dir,
+            config=cfg.to_dict(),
+            # TODO(rcadene): try set to True
+            save_code=False,
+            # TODO(rcadene): split train and eval, and run async eval with job_type="eval"
+            job_type="train_eval",
+            resume="must" if cfg.resume else None,
+        )
+        print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
+        logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
+        self._wandb = wandb
+
+    def log_policy(self, checkpoint_dir: Path):
+        """Checkpoints the policy to wandb."""
+        if self.cfg.disable_artifact:
+            return
+
+        step_id = checkpoint_dir.name
+        artifact_name = f"{self._group}-{step_id}"
+        artifact_name = get_safe_wandb_artifact_name(artifact_name)
+        artifact = self._wandb.Artifact(artifact_name, type="model")
+        artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE)
+        self._wandb.log_artifact(artifact)
+
+    def log_dict(self, d: dict, step: int, mode: str = "train"):
+        if mode in {"train", "eval"}:
+            raise ValueError(mode)
+
+        for k, v in d.items():
+            if not isinstance(v, (int, float, str)):
+                logging.warning(
+                    f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
+                )
+                continue
+            self._wandb.log({f"{mode}/{k}": v}, step=step)
+
+    def log_video(self, video_path: str, step: int, mode: str = "train"):
+        if mode in {"train", "eval"}:
+            raise ValueError(mode)
+
+        wandb_video = self._wandb.Video(video_path, fps=self.env_fps, format="mp4")
+        self._wandb.log({f"{mode}/video": wandb_video}, step=step)
diff --git a/lerobot/configs/train.py b/lerobot/configs/train.py
index 3d976e81..9d63339d 100644
--- a/lerobot/configs/train.py
+++ b/lerobot/configs/train.py
@@ -21,68 +21,6 @@ from lerobot.configs.policies import PreTrainedConfig
 TRAIN_CONFIG_NAME = "train_config.json"
 
 
-@dataclass
-class OfflineConfig:
-    steps: int = 100_000
-
-
-@dataclass
-class OnlineConfig:
-    """
-    The online training loop looks something like:
-
-    ```python
-    for i in range(steps):
-        do_online_rollout_and_update_online_buffer()
-        for j in range(steps_between_rollouts):
-            batch = next(dataloader_with_offline_and_online_data)
-            loss = policy(batch)
-            loss.backward()
-            optimizer.step()
-    ```
-
-    Note that the online training loop adopts most of the options from the offline loop unless specified
-    otherwise.
-    """
-
-    steps: int = 0
-    # How many episodes to collect at once when we reach the online rollout part of the training loop.
-    rollout_n_episodes: int = 1
-    # The number of environments to use in the gym.vector.VectorEnv. This ends up also being the batch size for
-    # the policy. Ideally you should set this to by an even divisor of rollout_n_episodes.
-    rollout_batch_size: int = 1
-    # How many optimization steps (forward, backward, optimizer step) to do between running rollouts.
-    steps_between_rollouts: int | None = None
-    # The proportion of online samples (vs offline samples) to include in the online training batches.
-    sampling_ratio: float = 0.5
-    # First seed to use for the online rollout environment. Seeds for subsequent rollouts are incremented by 1.
-    env_seed: int | None = None
-    # Sets the maximum number of frames that are stored in the online buffer for online training. The buffer is
-    # FIFO.
-    buffer_capacity: int | None = None
-    # The minimum number of frames to have in the online buffer before commencing online training.
-    # If buffer_seed_size > rollout_n_episodes, the rollout will be run multiple times until the
-    # seed size condition is satisfied.
-    buffer_seed_size: int = 0
-    # Whether to run the online rollouts asynchronously. This means we can run the online training steps in
-    # parallel with the rollouts. This might be advised if your GPU has the bandwidth to handle training
-    # + eval + environment rendering simultaneously.
-    do_rollout_async: bool = False
-
-    def __post_init__(self):
-        if self.steps == 0:
-            return
-
-        if self.steps_between_rollouts is None:
-            raise ValueError(
-                "'steps_between_rollouts' must be set to a positive integer, but it is currently None."
-            )
-        if self.env_seed is None:
-            raise ValueError("'env_seed' must be set to a positive integer, but it is currently None.")
-        if self.buffer_capacity is None:
-            raise ValueError("'buffer_capacity' must be set to a positive integer, but it is currently None.")
-
-
 @dataclass
 class TrainPipelineConfig(HubMixin):
     dataset: DatasetConfig
@@ -107,13 +45,12 @@ class TrainPipelineConfig(HubMixin):
     # Number of workers for the dataloader.
     num_workers: int = 4
     batch_size: int = 8
+    steps: int = 100_000
     eval_freq: int = 20_000
     log_freq: int = 200
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.
     save_freq: int = 20_000
-    offline: OfflineConfig = field(default_factory=OfflineConfig)
-    online: OnlineConfig = field(default_factory=OnlineConfig)
     use_policy_training_preset: bool = True
     optimizer: OptimizerConfig | None = None
     scheduler: LRSchedulerConfig | None = None
@@ -168,11 +105,8 @@ class TrainPipelineConfig(HubMixin):
             train_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}"
             self.output_dir = Path("outputs/train") / train_dir
 
-        if self.online.steps > 0:
-            if isinstance(self.dataset.repo_id, list):
-                raise NotImplementedError("Online training with LeRobotMultiDataset is not implemented.")
-            if self.env is None:
-                raise ValueError("An environment is required for online training")
+        if isinstance(self.dataset.repo_id, list):
+            raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")
 
         if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):
             raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.")
@@ -185,6 +119,9 @@ class TrainPipelineConfig(HubMixin):
         """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
         return ["policy"]
 
+    def to_dict(self) -> dict:
+        return draccus.encode(self)
+
     def _save_pretrained(self, save_directory: Path) -> None:
         with open(save_directory / TRAIN_CONFIG_NAME, "w") as f, draccus.config_type("json"):
             draccus.dump(self, f, indent=4)
diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py
index 253bc45c..7318748f 100644
--- a/lerobot/scripts/eval.py
+++ b/lerobot/scripts/eval.py
@@ -61,21 +61,21 @@ import einops
 import gymnasium as gym
 import numpy as np
 import torch
+from termcolor import colored
 from torch import Tensor, nn
 from tqdm import trange
 
 from lerobot.common.envs.factory import make_env
 from lerobot.common.envs.utils import preprocess_observation
-from lerobot.common.logger import log_output_dir
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.utils import get_device_from_parameters
 from lerobot.common.utils.io_utils import write_video
+from lerobot.common.utils.random_utils import set_seed
 from lerobot.common.utils.utils import (
     get_safe_torch_device,
     init_logging,
     inside_slurm,
-    set_global_seed,
 )
 from lerobot.configs import parser
 from lerobot.configs.eval import EvalPipelineConfig
@@ -125,9 +125,6 @@ def rollout(
     # Reset the policy and environments.
     policy.reset()
 
-    if hasattr(policy, "use_ema_modules"):
-        policy.use_ema_modules()
-
     observation, info = env.reset(seed=seeds)
     if render_callback is not None:
         render_callback(env)
@@ -463,9 +460,9 @@ def eval(cfg: EvalPipelineConfig):
 
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
-    set_global_seed(cfg.seed)
+    set_seed(cfg.seed)
 
-    log_output_dir(cfg.output_dir)
+    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
 
     logging.info("Making environment.")
     env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py
index 9af1a972..a840b33d 100644
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -15,58 +15,61 @@
 # limitations under the License.
 import logging
 import time
-from concurrent.futures import ThreadPoolExecutor
 from contextlib import nullcontext
-from copy import deepcopy
-from dataclasses import asdict
 from pprint import pformat
-from threading import Lock
+from typing import Any
 
-import numpy as np
 import torch
+from termcolor import colored
 from torch.amp import GradScaler
+from torch.optim import Optimizer
 
-from lerobot.common.datasets.factory import make_dataset, resolve_delta_timestamps
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.online_buffer import OnlineBuffer, compute_sampler_weights
+from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.sampler import EpisodeAwareSampler
 from lerobot.common.datasets.utils import cycle
 from lerobot.common.envs.factory import make_env
-from lerobot.common.logger import Logger, log_output_dir
-from lerobot.common.optim.factory import load_training_state, make_optimizer_and_scheduler
+from lerobot.common.optim.factory import make_optimizer_and_scheduler
 from lerobot.common.policies.factory import make_policy
+from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.utils import get_device_from_parameters
+from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
+from lerobot.common.utils.random_utils import set_seed
+from lerobot.common.utils.train_utils import (
+    get_step_checkpoint_dir,
+    get_step_identifier,
+    load_training_state,
+    save_checkpoint,
+    update_last_checkpoint,
+)
 from lerobot.common.utils.utils import (
     format_big_number,
-    get_safe_dtype,
     get_safe_torch_device,
     has_method,
     init_logging,
-    set_global_seed,
 )
+from lerobot.common.utils.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.scripts.eval import eval_policy
 
 
 def update_policy(
-    policy,
-    batch,
-    optimizer,
-    grad_clip_norm,
+    train_metrics: MetricsTracker,
+    policy: PreTrainedPolicy,
+    batch: Any,
+    optimizer: Optimizer,
+    grad_clip_norm: float,
     grad_scaler: GradScaler,
     lr_scheduler=None,
     use_amp: bool = False,
     lock=None,
-):
-    """Returns a dictionary of items for logging."""
+) -> tuple[MetricsTracker, dict]:
     start_time = time.perf_counter()
     device = get_device_from_parameters(policy)
     policy.train()
     with torch.autocast(device_type=device.type) if use_amp else nullcontext():
-        output_dict = policy.forward(batch)
+        loss, output_dict = policy.forward(batch)
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-        loss = output_dict["loss"]
     grad_scaler.scale(loss).backward()
 
     # Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
@@ -87,9 +90,6 @@ def update_policy(
 
     optimizer.zero_grad()
 
-    if hasattr(policy, "update_ema_modules"):
-        policy.update_ema_modules()
-
     # Step through pytorch scheduler at every batch instead of epoch
     if lr_scheduler is not None:
         lr_scheduler.step()
@@ -98,113 +98,34 @@ def update_policy(
         # To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
         policy.update()
 
-    info = {
-        "loss": loss.item(),
-        "grad_norm": float(grad_norm),
-        "lr": optimizer.param_groups[0]["lr"],
-        "update_s": time.perf_counter() - start_time,
-        **{k: v for k, v in output_dict.items() if k != "loss"},
-    }
-    info.update({k: v for k, v in output_dict.items() if k not in info})
-
-    return info
-
-
-def log_train_info(
-    logger: Logger, info: dict, step: int, cfg: TrainPipelineConfig, dataset: LeRobotDataset, is_online: bool
-):
-    loss = info["loss"]
-    grad_norm = info["grad_norm"]
-    lr = info["lr"]
-    update_s = info["update_s"]
-    dataloading_s = info["dataloading_s"]
-
-    # A sample is an (observation,action) pair, where observation and action
-    # can be on multiple timestamps. In a batch, we have `batch_size`` number of samples.
-    num_samples = (step + 1) * cfg.batch_size
-    avg_samples_per_ep = dataset.num_frames / dataset.num_episodes
-    num_episodes = num_samples / avg_samples_per_ep
-    num_epochs = num_samples / dataset.num_frames
-    log_items = [
-        f"step:{format_big_number(step)}",
-        # number of samples seen during training
-        f"smpl:{format_big_number(num_samples)}",
-        # number of episodes seen during training
-        f"ep:{format_big_number(num_episodes)}",
-        # number of time all unique samples are seen
-        f"epch:{num_epochs:.2f}",
-        f"loss:{loss:.3f}",
-        f"grdn:{grad_norm:.3f}",
-        f"lr:{lr:0.1e}",
-        # in seconds
-        f"updt_s:{update_s:.3f}",
-        f"data_s:{dataloading_s:.3f}",  # if not ~0, you are bottlenecked by cpu or io
-    ]
-    logging.info(" ".join(log_items))
-
-    info["step"] = step
-    info["num_samples"] = num_samples
-    info["num_episodes"] = num_episodes
-    info["num_epochs"] = num_epochs
-    info["is_online"] = is_online
-
-    logger.log_dict(info, step, mode="train")
-
-
-def log_eval_info(logger, info, step, cfg, dataset, is_online):
-    eval_s = info["eval_s"]
-    avg_sum_reward = info["avg_sum_reward"]
-    pc_success = info["pc_success"]
-
-    # A sample is an (observation,action) pair, where observation and action
-    # can be on multiple timestamps. In a batch, we have `batch_size`` number of samples.
-    num_samples = (step + 1) * cfg.batch_size
-    avg_samples_per_ep = dataset.num_frames / dataset.num_episodes
-    num_episodes = num_samples / avg_samples_per_ep
-    num_epochs = num_samples / dataset.num_frames
-    log_items = [
-        f"step:{format_big_number(step)}",
-        # number of samples seen during training
-        f"smpl:{format_big_number(num_samples)}",
-        # number of episodes seen during training
-        f"ep:{format_big_number(num_episodes)}",
-        # number of time all unique samples are seen
-        f"epch:{num_epochs:.2f}",
-        f"∑rwrd:{avg_sum_reward:.3f}",
-        f"success:{pc_success:.1f}%",
-        f"eval_s:{eval_s:.3f}",
-    ]
-    logging.info(" ".join(log_items))
-
-    info["step"] = step
-    info["num_samples"] = num_samples
-    info["num_episodes"] = num_episodes
-    info["num_epochs"] = num_epochs
-    info["is_online"] = is_online
-
-    logger.log_dict(info, step, mode="eval")
+    train_metrics.loss = loss.item()
+    train_metrics.grad_norm = grad_norm.item()
+    train_metrics.lr = optimizer.param_groups[0]["lr"]
+    train_metrics.update_s = time.perf_counter() - start_time
+    return train_metrics, output_dict
 
 
 @parser.wrap()
 def train(cfg: TrainPipelineConfig):
     cfg.validate()
+    logging.info(pformat(cfg.to_dict()))
 
-    logging.info(pformat(asdict(cfg)))
-
-    # log metrics to terminal and wandb
-    logger = Logger(cfg)
+    if cfg.wandb.enable and cfg.wandb.project:
+        wandb_logger = WandBLogger(cfg)
+    else:
+        wandb_logger = None
+        logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
 
     if cfg.seed is not None:
-        set_global_seed(cfg.seed)
+        set_seed(cfg.seed)
 
     # Check device is available
     device = get_safe_torch_device(cfg.device, log=True)
-
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
 
     logging.info("Creating dataset")
-    offline_dataset = make_dataset(cfg)
+    dataset = make_dataset(cfg)
 
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
@@ -218,7 +139,7 @@ def train(cfg: TrainPipelineConfig):
     policy = make_policy(
         cfg=cfg.policy,
         device=device,
-        ds_meta=offline_dataset.meta,
+        ds_meta=dataset.meta,
     )
 
     logging.info("Creating optimizer and scheduler")
@@ -233,65 +154,29 @@ def train(cfg: TrainPipelineConfig):
     num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
     num_total_params = sum(p.numel() for p in policy.parameters())
 
-    log_output_dir(cfg.output_dir)
+    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
     if cfg.env is not None:
         logging.info(f"{cfg.env.task=}")
-    logging.info(f"{cfg.offline.steps=} ({format_big_number(cfg.offline.steps)})")
-    logging.info(f"{cfg.online.steps=}")
-    logging.info(f"{offline_dataset.num_frames=} ({format_big_number(offline_dataset.num_frames)})")
-    logging.info(f"{offline_dataset.num_episodes=}")
+    logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
+    logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
+    logging.info(f"{dataset.num_episodes=}")
     logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
     logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
 
-    # Note: this helper will be used in offline and online training loops.
-    def evaluate_and_checkpoint_if_needed(step: int, is_online: bool):
-        _num_digits = max(6, len(str(cfg.offline.steps + cfg.online.steps)))
-        step_identifier = f"{step:0{_num_digits}d}"
-
-        if cfg.env is not None and cfg.eval_freq > 0 and step % cfg.eval_freq == 0:
-            logging.info(f"Eval policy at step {step}")
-            with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
-                eval_info = eval_policy(
-                    eval_env,
-                    policy,
-                    cfg.eval.n_episodes,
-                    videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_identifier}",
-                    max_episodes_rendered=4,
-                    start_seed=cfg.seed,
-                )
-            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_online=is_online)
-            if cfg.wandb.enable:
-                logger.log_video(eval_info["video_paths"][0], step, mode="eval")
-            logging.info("Resume training")
-
-        if cfg.save_checkpoint and (
-            step % cfg.save_freq == 0 or step == cfg.offline.steps + cfg.online.steps
-        ):
-            logging.info(f"Checkpoint policy after step {step}")
-            # Note: Save with step as the identifier, and format it to have at least 6 digits but more if
-            # needed (choose 6 as a minimum for consistency without being overkill).
-            logger.save_checkpoint(
-                step,
-                step_identifier,
-                policy,
-                optimizer,
-                lr_scheduler,
-            )
-            logging.info("Resume training")
-
     # create dataloader for offline training
-    if getattr(cfg.policy, "drop_n_last_frames", None):
+    if hasattr(cfg.policy, "drop_n_last_frames"):
         shuffle = False
         sampler = EpisodeAwareSampler(
-            offline_dataset.episode_data_index,
+            dataset.episode_data_index,
             drop_n_last_frames=cfg.policy.drop_n_last_frames,
             shuffle=True,
         )
     else:
         shuffle = True
         sampler = None
+
     dataloader = torch.utils.data.DataLoader(
-        offline_dataset,
+        dataset,
         num_workers=cfg.num_workers,
         batch_size=cfg.batch_size,
         shuffle=shuffle,
@@ -303,23 +188,30 @@ def train(cfg: TrainPipelineConfig):
 
     policy.train()
 
-    if hasattr(policy, "init_ema_modules"):
-        policy.init_ema_modules()
+    train_metrics = {
+        "loss": AverageMeter("loss", ":.3f"),
+        "grad_norm": AverageMeter("grdn", ":.3f"),
+        "lr": AverageMeter("lr", ":0.1e"),
+        "update_s": AverageMeter("updt_s", ":.3f"),
+        "dataloading_s": AverageMeter("data_s", ":.3f"),
+    }
 
-    offline_step = 0
-    for _ in range(step, cfg.offline.steps):
-        if offline_step == 0:
-            logging.info("Start offline training on a fixed dataset")
+    train_tracker = MetricsTracker(
+        cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step
+    )
 
+    logging.info("Start offline training on a fixed dataset")
+    for _ in range(step, cfg.steps):
         start_time = time.perf_counter()
         batch = next(dl_iter)
-        dataloading_s = time.perf_counter() - start_time
+        train_tracker.dataloading_s = time.perf_counter() - start_time
 
         for key in batch:
             if isinstance(batch[key], torch.Tensor):
                 batch[key] = batch[key].to(device, non_blocking=True)
 
-        train_info = update_policy(
+        train_tracker, output_dict = update_policy(
+            train_tracker,
             policy,
             batch,
             optimizer,
@@ -329,231 +221,57 @@ def train(cfg: TrainPipelineConfig):
             use_amp=cfg.use_amp,
         )
 
-        train_info["dataloading_s"] = dataloading_s
-
-        if step % cfg.log_freq == 0:
-            log_train_info(logger, train_info, step, cfg, offline_dataset, is_online=False)
-
-        # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
-        # so we pass in step + 1.
-        evaluate_and_checkpoint_if_needed(step + 1, is_online=False)
-
+        # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
+        # increment `step` here.
         step += 1
-        offline_step += 1  # noqa: SIM113
+        train_tracker.step()
+        is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0
+        is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
+        is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
 
-    if cfg.online.steps == 0:
-        if eval_env:
-            eval_env.close()
-        logging.info("End of training")
-        return
+        if is_log_step:
+            logging.info(train_tracker)
+            if wandb_logger:
+                wandb_log_dict = {**train_tracker.to_dict(), **output_dict}
+                wandb_logger.log_dict(wandb_log_dict)
+            train_tracker.reset_averages()
 
-    # Online training.
+        if cfg.save_checkpoint and is_saving_step:
+            logging.info(f"Checkpoint policy after step {step}")
+            checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
+            save_checkpoint(checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler)
+            update_last_checkpoint(checkpoint_dir)
+            if wandb_logger:
+                wandb_logger.log_policy(checkpoint_dir)
 
-    # Create an env dedicated to online episodes collection from policy rollout.
-    online_env = make_env(cfg.env, n_envs=cfg.online.rollout_batch_size)
-    delta_timestamps = resolve_delta_timestamps(cfg.policy, offline_dataset.meta)
-    online_buffer_path = logger.log_dir / "online_buffer"
-    if cfg.resume and not online_buffer_path.exists():
-        # If we are resuming a run, we default to the data shapes and buffer capacity from the saved online
-        # buffer.
-        logging.warning(
-            "When online training is resumed, we load the latest online buffer from the prior run, "
-            "and this might not coincide with the state of the buffer as it was at the moment the checkpoint "
-            "was made. This is because the online buffer is updated on disk during training, independently "
-            "of our explicit checkpointing mechanisms."
-        )
-    online_dataset = OnlineBuffer(
-        online_buffer_path,
-        data_spec={
-            **{
-                key: {"shape": ft.shape, "dtype": np.dtype("float32")}
-                for key, ft in policy.config.input_features.items()
-            },
-            **{
-                key: {"shape": ft.shape, "dtype": np.dtype("float32")}
-                for key, ft in policy.config.output_features.items()
-            },
-            "next.reward": {"shape": (), "dtype": np.dtype("float32")},
-            "next.done": {"shape": (), "dtype": np.dtype("?")},
-            "task_index": {"shape": (), "dtype": np.dtype("int64")},
-            # FIXME: 'task' is a string
-            # "task": {"shape": (), "dtype": np.dtype("?")},
-            # FIXME: 'next.success' is expected by pusht env but not xarm
-            "next.success": {"shape": (), "dtype": np.dtype("?")},
-        },
-        buffer_capacity=cfg.online.buffer_capacity,
-        fps=online_env.unwrapped.metadata["render_fps"],
-        delta_timestamps=delta_timestamps,
-    )
-
-    # If we are doing online rollouts asynchronously, deepcopy the policy to use for online rollouts (this
-    # makes it possible to do online rollouts in parallel with training updates).
-    online_rollout_policy = deepcopy(policy) if cfg.online.do_rollout_async else policy
-
-    # Create dataloader for online training.
-    concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
-    sampler_weights = compute_sampler_weights(
-        offline_dataset,
-        offline_drop_n_last_frames=getattr(cfg.policy, "drop_n_last_frames", 0),
-        online_dataset=online_dataset,
-        # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have
-        # this final observation in the offline datasets, but we might add them in future.
-        online_drop_n_last_frames=getattr(cfg.policy, "drop_n_last_frames", 0) + 1,
-        online_sampling_ratio=cfg.online.sampling_ratio,
-    )
-    sampler = torch.utils.data.WeightedRandomSampler(
-        sampler_weights,
-        num_samples=len(concat_dataset),
-        replacement=True,
-    )
-    dataloader = torch.utils.data.DataLoader(
-        concat_dataset,
-        batch_size=cfg.batch_size,
-        num_workers=cfg.num_workers,
-        sampler=sampler,
-        pin_memory=device.type != "cpu",
-        drop_last=True,
-    )
-    dl_iter = cycle(dataloader)
-
-    if cfg.online.do_rollout_async:
-        # Lock and thread pool executor for asynchronous online rollouts.
-        lock = Lock()
-        # Note: 1 worker because we only ever want to run one set of online rollouts at a time. Batch
-        # parallelization of rollouts is handled within the job.
-        executor = ThreadPoolExecutor(max_workers=1)
-    else:
-        lock = None
-
-    online_step = 0
-    online_rollout_s = 0  # time take to do online rollout
-    update_online_buffer_s = 0  # time taken to update the online buffer with the online rollout data
-    # Time taken waiting for the online buffer to finish being updated. This is relevant when using the async
-    # online rollout option.
-    await_update_online_buffer_s = 0
-    rollout_start_seed = cfg.online.env_seed
-
-    while True:
-        if online_step == cfg.online.steps:
-            break
-
-        if online_step == 0:
-            logging.info("Start online training by interacting with environment")
-
-        def sample_trajectory_and_update_buffer():
-            nonlocal rollout_start_seed
-
-            with lock if lock is not None else nullcontext():
-                online_rollout_policy.load_state_dict(policy.state_dict())
-
-            online_rollout_policy.eval()
-            start_rollout_time = time.perf_counter()
-
-            with torch.no_grad():
+        if cfg.env and is_eval_step:
+            step_id = get_step_identifier(step, cfg.steps)
+            logging.info(f"Eval policy at step {step}")
+            with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
                 eval_info = eval_policy(
-                    online_env,
-                    online_rollout_policy,
-                    n_episodes=cfg.online.rollout_n_episodes,
-                    max_episodes_rendered=min(10, cfg.online.rollout_n_episodes),
-                    videos_dir=logger.log_dir / "online_rollout_videos",
-                    return_episode_data=True,
-                    start_seed=(rollout_start_seed := (rollout_start_seed + cfg.batch_size) % 1000000),
+                    eval_env,
+                    policy,
+                    cfg.eval.n_episodes,
+                    videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
+                    max_episodes_rendered=4,
+                    start_seed=cfg.seed,
                 )
-            online_rollout_s = time.perf_counter() - start_rollout_time
 
-            if len(offline_dataset.meta.tasks) > 1:
-                raise NotImplementedError("Add support for multi task.")
-
-            # TODO(rcadene, aliberts): Hack to add a task to the online_dataset (0 is the first task of the offline_dataset)
-            total_num_frames = eval_info["episodes"]["index"].shape[0]
-            eval_info["episodes"]["task_index"] = torch.tensor([0] * total_num_frames, dtype=torch.int64)
-            eval_info["episodes"]["task"] = ["do the thing"] * total_num_frames
-
-            with lock if lock is not None else nullcontext():
-                start_update_buffer_time = time.perf_counter()
-                online_dataset.add_data(eval_info["episodes"])
-
-                # Update the concatenated dataset length used during sampling.
-                concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
-
-                # Update the sampling weights.
-                sampler.weights = compute_sampler_weights(
-                    offline_dataset,
-                    offline_drop_n_last_frames=getattr(cfg.policy, "drop_n_last_frames", 0),
-                    online_dataset=online_dataset,
-                    # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have
-                    # this final observation in the offline datasets, but we might add them in future.
-                    online_drop_n_last_frames=getattr(cfg.policy, "drop_n_last_frames", 0) + 1,
-                    online_sampling_ratio=cfg.online.sampling_ratio,
-                )
-                sampler.num_frames = len(concat_dataset)
-
-                update_online_buffer_s = time.perf_counter() - start_update_buffer_time
-
-            return online_rollout_s, update_online_buffer_s
-
-        if lock is None:
-            online_rollout_s, update_online_buffer_s = sample_trajectory_and_update_buffer()
-        else:
-            future = executor.submit(sample_trajectory_and_update_buffer)
-            # If we aren't doing async rollouts, or if we haven't yet gotten enough examples in our buffer, wait
-            # here until the rollout and buffer update is done, before proceeding to the policy update steps.
-            if len(online_dataset) <= cfg.online.buffer_seed_size:
-                online_rollout_s, update_online_buffer_s = future.result()
-
-        if len(online_dataset) <= cfg.online.buffer_seed_size:
-            logging.info(f"Seeding online buffer: {len(online_dataset)}/{cfg.online.buffer_seed_size}")
-            continue
-
-        policy.train()
-        for _ in range(cfg.online.steps_between_rollouts):
-            with lock if lock is not None else nullcontext():
-                start_time = time.perf_counter()
-                batch = next(dl_iter)
-                dataloading_s = time.perf_counter() - start_time
-
-            for key in batch:
-                if isinstance(batch[key], torch.Tensor):
-                    dtype = get_safe_dtype(batch[key].dtype, device)
-                    batch[key] = batch[key].to(device=device, dtype=dtype, non_blocking=True)
-
-            train_info = update_policy(
-                policy,
-                batch,
-                optimizer,
-                cfg.optimizer.grad_clip_norm,
-                grad_scaler=grad_scaler,
-                lr_scheduler=lr_scheduler,
-                use_amp=cfg.use_amp,
-                lock=lock,
+            eval_metrics = {
+                "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),
+                "pc_success": AverageMeter("success", ":.1f"),
+                "eval_s": AverageMeter("eval_s", ":.3f"),
+            }
+            eval_tracker = MetricsTracker(
+                cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step
             )
-
-            train_info["dataloading_s"] = dataloading_s
-            train_info["online_rollout_s"] = online_rollout_s
-            train_info["update_online_buffer_s"] = update_online_buffer_s
-            train_info["await_update_online_buffer_s"] = await_update_online_buffer_s
-            with lock if lock is not None else nullcontext():
-                train_info["online_buffer_size"] = len(online_dataset)
-
-            if step % cfg.log_freq == 0:
-                log_train_info(logger, train_info, step, cfg, online_dataset, is_online=True)
-
-            # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
-            # so we pass in step + 1.
-            evaluate_and_checkpoint_if_needed(step + 1, is_online=True)
-
-            step += 1
-            online_step += 1
-
-        # If we're doing async rollouts, we should now wait until we've completed them before proceeding
-        # to do the next batch of rollouts.
-        if cfg.online.do_rollout_async and future.running():
-            start = time.perf_counter()
-            online_rollout_s, update_online_buffer_s = future.result()
-            await_update_online_buffer_s = time.perf_counter() - start
-
-        if online_step >= cfg.online.steps:
-            break
+            eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s")
+            eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward")
+            eval_tracker.pc_success = eval_info["aggregated"].pop("pc_success")
+            logging.info(eval_tracker)
+            if wandb_logger:
+                wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
+                wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
 
     if eval_env:
         eval_env.close()
diff --git a/poetry.lock b/poetry.lock
index 7f53cfb0..bfa3f084 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1257,12 +1257,14 @@ pyarrow = "*"
 
 [[package]]
 name = "draccus"
-version = "0.9.3"
+version = "0.10.0"
 description = "A slightly opinionated framework for simple dataclass-based configurations based on Pyrallis."
 optional = false
-python-versions = ">=3.8"
-files = []
-develop = false
+python-versions = ">=3.9"
+files = [
+    {file = "draccus-0.10.0-py3-none-any.whl", hash = "sha256:90243418ae0e9271c390a59cafb6acfd37001193696ed36fcc8525f791a83282"},
+    {file = "draccus-0.10.0.tar.gz", hash = "sha256:8dd08304219becdcd66cd16058ba98e9c3e6b7bfe48ccb9579dae39f8d37ae19"},
+]
 
 [package.dependencies]
 mergedeep = ">=1.3,<2.0"
@@ -1274,12 +1276,6 @@ typing-inspect = ">=0.9.0,<0.10.0"
 [package.extras]
 dev = ["black", "mypy", "pre-commit", "pytest", "ruff"]
 
-[package.source]
-type = "git"
-url = "https://github.com/dlwh/draccus.git"
-reference = "HEAD"
-resolved_reference = "9b690730ca108930519f48cc5dead72a72fd27cb"
-
 [[package]]
 name = "drawnow"
 version = "0.72.5"
@@ -4911,8 +4907,6 @@ files = [
     {file = "PyAudio-0.2.14-cp311-cp311-win_amd64.whl", hash = "sha256:bbeb01d36a2f472ae5ee5e1451cacc42112986abe622f735bb870a5db77cf903"},
     {file = "PyAudio-0.2.14-cp312-cp312-win32.whl", hash = "sha256:5fce4bcdd2e0e8c063d835dbe2860dac46437506af509353c7f8114d4bacbd5b"},
     {file = "PyAudio-0.2.14-cp312-cp312-win_amd64.whl", hash = "sha256:12f2f1ba04e06ff95d80700a78967897a489c05e093e3bffa05a84ed9c0a7fa3"},
-    {file = "PyAudio-0.2.14-cp313-cp313-win32.whl", hash = "sha256:95328285b4dab57ea8c52a4a996cb52be6d629353315be5bfda403d15932a497"},
-    {file = "PyAudio-0.2.14-cp313-cp313-win_amd64.whl", hash = "sha256:692d8c1446f52ed2662120bcd9ddcb5aa2b71f38bda31e58b19fb4672fffba69"},
     {file = "PyAudio-0.2.14-cp38-cp38-win32.whl", hash = "sha256:858caf35b05c26d8fc62f1efa2e8f53d5fa1a01164842bd622f70ddc41f55000"},
     {file = "PyAudio-0.2.14-cp38-cp38-win_amd64.whl", hash = "sha256:2dac0d6d675fe7e181ba88f2de88d321059b69abd52e3f4934a8878e03a7a074"},
     {file = "PyAudio-0.2.14-cp39-cp39-win32.whl", hash = "sha256:f745109634a7c19fa4d6b8b7d6967c3123d988c9ade0cd35d4295ee1acdb53e9"},
@@ -6934,7 +6928,7 @@ test = ["pytest", "ruff"]
 name = "tokenizers"
 version = "0.21.0"
 description = ""
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "tokenizers-0.21.0-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:3c4c93eae637e7d2aaae3d376f06085164e1660f89304c0ab2b1d08a406636b2"},
@@ -7168,7 +7162,7 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 name = "transformers"
 version = "4.48.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
-optional = false
+optional = true
 python-versions = ">=3.9.0"
 files = [
     {file = "transformers-4.48.0-py3-none-any.whl", hash = "sha256:6d3de6d71cb5f2a10f9775ccc17abce9620195caaf32ec96542bd2a6937f25b0"},
@@ -7936,4 +7930,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "089df92a0455bb58d2f600ad645b99362c9ff2b800a0c6108edc09f51509c716"
+content-hash = "3df78b6f373b1e4ee79530932f386fb4cfd302c1ceffe26617d26fb1a7e751ce"
diff --git a/pyproject.toml b/pyproject.toml
index ec30aaea..4469f5ce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -69,8 +69,8 @@ pyrender = {git = "https://github.com/mmatl/pyrender.git", markers = "sys_platfo
 hello-robot-stretch-body = {version = ">=0.7.27", markers = "sys_platform == 'linux'", optional = true}
 pyserial = {version = ">=3.5", optional = true}
 jsonlines = ">=4.0.0"
-transformers = ">=4.48.0"
-draccus = {git = "https://github.com/dlwh/draccus.git"} # replace with draccus = ">=0.9.4" when https://github.com/dlwh/draccus/pull/29 is in release
+transformers = {version = ">=4.48.0", optional = true}
+draccus = ">=0.10.0"
 
 
 [tool.poetry.extras]
diff --git a/tests/conftest.py b/tests/conftest.py
index b49bebd9..dc746142 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -28,6 +28,7 @@ pytest_plugins = [
     "tests.fixtures.dataset_factories",
     "tests.fixtures.files",
     "tests.fixtures.hub",
+    "tests.fixtures.optimizers",
 ]
 
 
diff --git a/tests/fixtures/optimizers.py b/tests/fixtures/optimizers.py
new file mode 100644
index 00000000..1a9b9d11
--- /dev/null
+++ b/tests/fixtures/optimizers.py
@@ -0,0 +1,26 @@
+import pytest
+import torch
+
+from lerobot.common.optim.optimizers import AdamConfig
+from lerobot.common.optim.schedulers import VQBeTSchedulerConfig
+
+
+@pytest.fixture
+def model_params():
+    return [torch.nn.Parameter(torch.randn(10, 10))]
+
+
+@pytest.fixture
+def optimizer(model_params):
+    optimizer = AdamConfig().build(model_params)
+    # Dummy step to populate state
+    loss = sum(param.sum() for param in model_params)
+    loss.backward()
+    optimizer.step()
+    return optimizer
+
+
+@pytest.fixture
+def scheduler(optimizer):
+    config = VQBeTSchedulerConfig(num_warmup_steps=10, num_vqvae_training_steps=20, num_cycles=0.5)
+    return config.build(optimizer, num_training_steps=100)
diff --git a/tests/scripts/save_image_transforms_to_safetensors.py b/tests/scripts/save_image_transforms_to_safetensors.py
index 8a011a22..bd2c3add 100644
--- a/tests/scripts/save_image_transforms_to_safetensors.py
+++ b/tests/scripts/save_image_transforms_to_safetensors.py
@@ -25,7 +25,7 @@ from lerobot.common.datasets.transforms import (
     ImageTransformsConfig,
     make_transform_from_config,
 )
-from lerobot.common.utils.utils import seeded_context
+from lerobot.common.utils.random_utils import seeded_context
 
 ARTIFACT_DIR = Path("tests/data/save_image_transforms_to_safetensors")
 DATASET_REPO_ID = "lerobot/aloha_mobile_shrimp"
diff --git a/tests/scripts/save_policy_to_safetensors.py b/tests/scripts/save_policy_to_safetensors.py
index 7bfdc6b1..de784db3 100644
--- a/tests/scripts/save_policy_to_safetensors.py
+++ b/tests/scripts/save_policy_to_safetensors.py
@@ -22,14 +22,14 @@ from safetensors.torch import save_file
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.optim.factory import make_optimizer_and_scheduler
 from lerobot.common.policies.factory import make_policy, make_policy_config
-from lerobot.common.utils.utils import set_global_seed
+from lerobot.common.utils.random_utils import set_seed
 from lerobot.configs.default import DatasetConfig
 from lerobot.configs.train import TrainPipelineConfig
 
 
 def get_policy_stats(ds_repo_id, env_name, policy_name, policy_kwargs, train_kwargs):
     # TODO(rcadene, aliberts): env_name?
-    set_global_seed(1337)
+    set_seed(1337)
 
     train_cfg = TrainPipelineConfig(
         # TODO(rcadene, aliberts): remove dataset download
@@ -53,9 +53,9 @@ def get_policy_stats(ds_repo_id, env_name, policy_name, policy_kwargs, train_kwa
     )
 
     batch = next(iter(dataloader))
-    output_dict = policy.forward(batch)
+    loss, output_dict = policy.forward(batch)
     output_dict = {k: v for k, v in output_dict.items() if isinstance(v, torch.Tensor)}
-    loss = output_dict["loss"]
+    output_dict["loss"] = loss
 
     loss.backward()
     grad_stats = {}
diff --git a/tests/test_control_robot.py b/tests/test_control_robot.py
index e80b507c..36ee096f 100644
--- a/tests/test_control_robot.py
+++ b/tests/test_control_robot.py
@@ -29,7 +29,6 @@ from unittest.mock import patch
 
 import pytest
 
-from lerobot.common.logger import Logger
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.robot_devices.control_configs import (
@@ -38,9 +37,7 @@ from lerobot.common.robot_devices.control_configs import (
     ReplayControlConfig,
     TeleoperateControlConfig,
 )
-from lerobot.configs.default import DatasetConfig
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.train import TrainPipelineConfig
 from lerobot.scripts.control_robot import calibrate, record, replay, teleoperate
 from tests.test_robots import make_robot
 from tests.utils import DEVICE, TEST_ROBOT_TYPES, mock_calibration_dir, require_robot
@@ -185,20 +182,8 @@ def test_record_and_replay_and_policy(tmpdir, request, robot_type, mock):
 
     out_dir = tmpdir / "logger"
 
-    ds_cfg = DatasetConfig(repo_id, local_files_only=True)
-    train_cfg = TrainPipelineConfig(
-        dataset=ds_cfg,
-        policy=policy_cfg,
-        output_dir=out_dir,
-        device=DEVICE,
-    )
-    logger = Logger(train_cfg)
-    logger.save_checkpoint(
-        train_step=0,
-        identifier=0,
-        policy=policy,
-    )
     pretrained_policy_path = out_dir / "checkpoints/last/pretrained_model"
+    policy.save_pretrained(pretrained_policy_path)
 
     # In `examples/9_use_aloha.md`, we advise using `num_image_writer_processes=1`
     # during inference, to reach constent fps, so we test this here.
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index b11534ee..2945df41 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -46,7 +46,7 @@ from lerobot.common.datasets.utils import (
 from lerobot.common.envs.factory import make_env_config
 from lerobot.common.policies.factory import make_policy_config
 from lerobot.common.robot_devices.robots.utils import make_robot
-from lerobot.common.utils.utils import seeded_context
+from lerobot.common.utils.random_utils import seeded_context
 from lerobot.configs.default import DatasetConfig
 from lerobot.configs.train import TrainPipelineConfig
 from tests.fixtures.constants import DUMMY_REPO_ID
diff --git a/tests/test_image_transforms.py b/tests/test_image_transforms.py
index 72a4ee6e..c118018a 100644
--- a/tests/test_image_transforms.py
+++ b/tests/test_image_transforms.py
@@ -28,7 +28,7 @@ from lerobot.common.datasets.transforms import (
     SharpnessJitter,
     make_transform_from_config,
 )
-from lerobot.common.utils.utils import seeded_context
+from lerobot.common.utils.random_utils import seeded_context
 from lerobot.scripts.visualize_image_transforms import (
     save_all_transforms,
     save_each_transform,
diff --git a/tests/test_io_utils.py b/tests/test_io_utils.py
new file mode 100644
index 00000000..d14f7adc
--- /dev/null
+++ b/tests/test_io_utils.py
@@ -0,0 +1,74 @@
+import json
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from lerobot.common.utils.io_utils import deserialize_json_into_object
+
+
+@pytest.fixture
+def tmp_json_file(tmp_path: Path):
+    """Writes `data` to a temporary JSON file and returns the file's path."""
+
+    def _write(data: Any) -> Path:
+        file_path = tmp_path / "data.json"
+        with file_path.open("w", encoding="utf-8") as f:
+            json.dump(data, f)
+        return file_path
+
+    return _write
+
+
+def test_simple_dict(tmp_json_file):
+    data = {"name": "Alice", "age": 30}
+    json_path = tmp_json_file(data)
+    obj = {"name": "", "age": 0}
+    assert deserialize_json_into_object(json_path, obj) == data
+
+
+def test_nested_structure(tmp_json_file):
+    data = {"items": [1, 2, 3], "info": {"active": True}}
+    json_path = tmp_json_file(data)
+    obj = {"items": [0, 0, 0], "info": {"active": False}}
+    assert deserialize_json_into_object(json_path, obj) == data
+
+
+def test_tuple_conversion(tmp_json_file):
+    data = {"coords": [10.5, 20.5]}
+    json_path = tmp_json_file(data)
+    obj = {"coords": (0.0, 0.0)}
+    result = deserialize_json_into_object(json_path, obj)
+    assert result["coords"] == (10.5, 20.5)
+
+
+def test_type_mismatch_raises(tmp_json_file):
+    data = {"numbers": {"bad": "structure"}}
+    json_path = tmp_json_file(data)
+    obj = {"numbers": [0, 0]}
+    with pytest.raises(TypeError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_missing_key_raises(tmp_json_file):
+    data = {"one": 1}
+    json_path = tmp_json_file(data)
+    obj = {"one": 0, "two": 0}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_extra_key_raises(tmp_json_file):
+    data = {"one": 1, "two": 2}
+    json_path = tmp_json_file(data)
+    obj = {"one": 0}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_list_length_mismatch_raises(tmp_json_file):
+    data = {"nums": [1, 2, 3]}
+    json_path = tmp_json_file(data)
+    obj = {"nums": [0, 0]}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)
diff --git a/tests/test_logging_utils.py b/tests/test_logging_utils.py
new file mode 100644
index 00000000..72385496
--- /dev/null
+++ b/tests/test_logging_utils.py
@@ -0,0 +1,107 @@
+import pytest
+
+from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
+
+
+@pytest.fixture
+def mock_metrics():
+    return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
+
+
+def test_average_meter_initialization():
+    meter = AverageMeter("loss", ":.2f")
+    assert meter.name == "loss"
+    assert meter.fmt == ":.2f"
+    assert meter.val == 0.0
+    assert meter.avg == 0.0
+    assert meter.sum == 0.0
+    assert meter.count == 0.0
+
+
+def test_average_meter_update():
+    meter = AverageMeter("accuracy")
+    meter.update(5, n=2)
+    assert meter.val == 5
+    assert meter.sum == 10
+    assert meter.count == 2
+    assert meter.avg == 5
+
+
+def test_average_meter_reset():
+    meter = AverageMeter("loss")
+    meter.update(3, 4)
+    meter.reset()
+    assert meter.val == 0.0
+    assert meter.avg == 0.0
+    assert meter.sum == 0.0
+    assert meter.count == 0.0
+
+
+def test_average_meter_str():
+    meter = AverageMeter("metric", ":.1f")
+    meter.update(4.567, 3)
+    assert str(meter) == "metric:4.6"
+
+
+def test_metrics_tracker_initialization(mock_metrics):
+    tracker = MetricsTracker(
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=10
+    )
+    assert tracker.steps == 10
+    assert tracker.samples == 10 * 32
+    assert tracker.episodes == tracker.samples / (1000 / 50)
+    assert tracker.epochs == tracker.samples / 1000
+    assert "loss" in tracker.metrics
+    assert "accuracy" in tracker.metrics
+
+
+def test_metrics_tracker_step(mock_metrics):
+    tracker = MetricsTracker(
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=5
+    )
+    tracker.step()
+    assert tracker.steps == 6
+    assert tracker.samples == 6 * 32
+    assert tracker.episodes == tracker.samples / (1000 / 50)
+    assert tracker.epochs == tracker.samples / 1000
+
+
+def test_metrics_tracker_getattr(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    assert tracker.loss == mock_metrics["loss"]
+    assert tracker.accuracy == mock_metrics["accuracy"]
+    with pytest.raises(AttributeError):
+        _ = tracker.non_existent_metric
+
+
+def test_metrics_tracker_setattr(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss = 2.0
+    assert tracker.loss.val == 2.0
+
+
+def test_metrics_tracker_str(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(3.456, 1)
+    tracker.accuracy.update(0.876, 1)
+    output = str(tracker)
+    assert "loss:3.456" in output
+    assert "accuracy:0.88" in output
+
+
+def test_metrics_tracker_to_dict(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(5, 2)
+    metrics_dict = tracker.to_dict()
+    assert isinstance(metrics_dict, dict)
+    assert metrics_dict["loss"] == 5  # average value
+    assert metrics_dict["steps"] == tracker.steps
+
+
+def test_metrics_tracker_reset_averages(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(10, 3)
+    tracker.accuracy.update(0.95, 5)
+    tracker.reset_averages()
+    assert tracker.loss.avg == 0.0
+    assert tracker.accuracy.avg == 0.0
diff --git a/tests/test_optimizers.py b/tests/test_optimizers.py
new file mode 100644
index 00000000..cf5c5b18
--- /dev/null
+++ b/tests/test_optimizers.py
@@ -0,0 +1,43 @@
+import pytest
+import torch
+
+from lerobot.common.constants import (
+    OPTIMIZER_PARAM_GROUPS,
+    OPTIMIZER_STATE,
+)
+from lerobot.common.optim.optimizers import (
+    AdamConfig,
+    AdamWConfig,
+    SGDConfig,
+    load_optimizer_state,
+    save_optimizer_state,
+)
+
+
+@pytest.mark.parametrize(
+    "config_cls, expected_class",
+    [
+        (AdamConfig, torch.optim.Adam),
+        (AdamWConfig, torch.optim.AdamW),
+        (SGDConfig, torch.optim.SGD),
+    ],
+)
+def test_optimizer_build(config_cls, expected_class, model_params):
+    config = config_cls()
+    optimizer = config.build(model_params)
+    assert isinstance(optimizer, expected_class)
+    assert optimizer.defaults["lr"] == config.lr
+
+
+def test_save_optimizer_state(optimizer, tmp_path):
+    save_optimizer_state(optimizer, tmp_path)
+    assert (tmp_path / OPTIMIZER_STATE).is_file()
+    assert (tmp_path / OPTIMIZER_PARAM_GROUPS).is_file()
+
+
+def test_save_and_load_optimizer_state(model_params, optimizer, tmp_path):
+    save_optimizer_state(optimizer, tmp_path)
+    loaded_optimizer = AdamConfig().build(model_params)
+    loaded_optimizer = load_optimizer_state(loaded_optimizer, tmp_path)
+
+    torch.testing.assert_close(optimizer.state_dict(), loaded_optimizer.state_dict())
diff --git a/tests/test_policies.py b/tests/test_policies.py
index a949a52c..4374157d 100644
--- a/tests/test_policies.py
+++ b/tests/test_policies.py
@@ -36,7 +36,7 @@ from lerobot.common.policies.factory import (
 )
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.utils.utils import seeded_context
+from lerobot.common.utils.random_utils import seeded_context
 from lerobot.configs.default import DatasetConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
diff --git a/tests/test_random_utils.py b/tests/test_random_utils.py
new file mode 100644
index 00000000..8eee2b68
--- /dev/null
+++ b/tests/test_random_utils.py
@@ -0,0 +1,109 @@
+import random
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.common.utils.random_utils import (
+    deserialize_numpy_rng_state,
+    deserialize_python_rng_state,
+    deserialize_rng_state,
+    deserialize_torch_rng_state,
+    get_rng_state,
+    seeded_context,
+    serialize_numpy_rng_state,
+    serialize_python_rng_state,
+    serialize_rng_state,
+    serialize_torch_rng_state,
+    set_rng_state,
+    set_seed,
+)
+
+
+@pytest.fixture
+def fixed_seed():
+    """Fixture to set a consistent initial seed for each test."""
+    set_seed(12345)
+    yield
+
+
+def test_serialize_deserialize_python_rng(fixed_seed):
+    # Save state after generating val1
+    _ = random.random()
+    st = serialize_python_rng_state()
+    # Next random is val2
+    val2 = random.random()
+    # Restore the state, so the next random should match val2
+    deserialize_python_rng_state(st)
+    val3 = random.random()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_numpy_rng(fixed_seed):
+    _ = np.random.rand()
+    st = serialize_numpy_rng_state()
+    val2 = np.random.rand()
+    deserialize_numpy_rng_state(st)
+    val3 = np.random.rand()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_torch_rng(fixed_seed):
+    _ = torch.rand(1).item()
+    st = serialize_torch_rng_state()
+    val2 = torch.rand(1).item()
+    deserialize_torch_rng_state(st)
+    val3 = torch.rand(1).item()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_rng(fixed_seed):
+    # Generate one from each library
+    _ = random.random()
+    _ = np.random.rand()
+    _ = torch.rand(1).item()
+    # Serialize
+    st = serialize_rng_state()
+    # Generate second set
+    val_py2 = random.random()
+    val_np2 = np.random.rand()
+    val_th2 = torch.rand(1).item()
+    # Restore, so the next draws should match val_py2, val_np2, val_th2
+    deserialize_rng_state(st)
+    assert random.random() == val_py2
+    assert np.random.rand() == val_np2
+    assert torch.rand(1).item() == val_th2
+
+
+def test_get_set_rng_state(fixed_seed):
+    st = get_rng_state()
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    # Change states
+    random.random()
+    np.random.rand()
+    torch.rand(1)
+    # Restore
+    set_rng_state(st)
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    assert val1 == val2
+
+
+def test_set_seed():
+    set_seed(1337)
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    set_seed(1337)
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    assert val1 == val2
+
+
+def test_seeded_context(fixed_seed):
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    with seeded_context(1337):
+        seeded_val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    with seeded_context(1337):
+        seeded_val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+
+    assert seeded_val1 == seeded_val2
+    assert all(a != b for a, b in zip(val1, seeded_val1, strict=True))  # changed inside the context
+    assert all(a != b for a, b in zip(val2, seeded_val2, strict=True))  # changed again after exiting
diff --git a/tests/test_schedulers.py b/tests/test_schedulers.py
new file mode 100644
index 00000000..e871fee1
--- /dev/null
+++ b/tests/test_schedulers.py
@@ -0,0 +1,81 @@
+from torch.optim.lr_scheduler import LambdaLR
+
+from lerobot.common.constants import SCHEDULER_STATE
+from lerobot.common.optim.schedulers import (
+    CosineDecayWithWarmupSchedulerConfig,
+    DiffuserSchedulerConfig,
+    VQBeTSchedulerConfig,
+    load_scheduler_state,
+    save_scheduler_state,
+)
+
+
+def test_diffuser_scheduler(optimizer):
+    config = DiffuserSchedulerConfig(name="cosine", num_warmup_steps=5)
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()  # so that we don't get torch warning
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.0002],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_vqbet_scheduler(optimizer):
+    config = VQBeTSchedulerConfig(num_warmup_steps=10, num_vqvae_training_steps=20, num_cycles=0.5)
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.001],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_cosine_decay_with_warmup_scheduler(optimizer):
+    config = CosineDecayWithWarmupSchedulerConfig(
+        num_warmup_steps=10, num_decay_steps=90, peak_lr=0.01, decay_lr=0.001
+    )
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.0001818181818181819],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_save_scheduler_state(scheduler, tmp_path):
+    save_scheduler_state(scheduler, tmp_path)
+    assert (tmp_path / SCHEDULER_STATE).is_file()
+
+
+def test_save_load_scheduler_state(scheduler, tmp_path):
+    save_scheduler_state(scheduler, tmp_path)
+    loaded_scheduler = load_scheduler_state(scheduler, tmp_path)
+
+    assert scheduler.state_dict() == loaded_scheduler.state_dict()
diff --git a/tests/test_train_utils.py b/tests/test_train_utils.py
new file mode 100644
index 00000000..d6ed0063
--- /dev/null
+++ b/tests/test_train_utils.py
@@ -0,0 +1,84 @@
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+from lerobot.common.constants import (
+    CHECKPOINTS_DIR,
+    LAST_CHECKPOINT_LINK,
+    OPTIMIZER_PARAM_GROUPS,
+    OPTIMIZER_STATE,
+    RNG_STATE,
+    SCHEDULER_STATE,
+    TRAINING_STATE_DIR,
+    TRAINING_STEP,
+)
+from lerobot.common.utils.train_utils import (
+    get_step_checkpoint_dir,
+    get_step_identifier,
+    load_training_state,
+    load_training_step,
+    save_checkpoint,
+    save_training_state,
+    save_training_step,
+    update_last_checkpoint,
+)
+
+
+def test_get_step_identifier():
+    assert get_step_identifier(5, 1000) == "000005"
+    assert get_step_identifier(123, 100_000) == "000123"
+    assert get_step_identifier(456789, 1_000_000) == "0456789"
+
+
+def test_get_step_checkpoint_dir():
+    output_dir = Path("/checkpoints")
+    step_dir = get_step_checkpoint_dir(output_dir, 1000, 5)
+    assert step_dir == output_dir / CHECKPOINTS_DIR / "000005"
+
+
+def test_save_load_training_step(tmp_path):
+    save_training_step(5000, tmp_path)
+    assert (tmp_path / TRAINING_STEP).is_file()
+
+
+def test_load_training_step(tmp_path):
+    step = 5000
+    save_training_step(step, tmp_path)
+    loaded_step = load_training_step(tmp_path)
+    assert loaded_step == step
+
+
+def test_update_last_checkpoint(tmp_path):
+    checkpoint = tmp_path / "0005"
+    checkpoint.mkdir()
+    update_last_checkpoint(checkpoint)
+    last_checkpoint = tmp_path / LAST_CHECKPOINT_LINK
+    assert last_checkpoint.is_symlink()
+    assert last_checkpoint.resolve() == checkpoint
+
+
+@patch("lerobot.common.utils.train_utils.save_training_state")
+def test_save_checkpoint(mock_save_training_state, tmp_path, optimizer):
+    policy = Mock()
+    cfg = Mock()
+    save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
+    policy.save_pretrained.assert_called_once()
+    cfg.save_pretrained.assert_called_once()
+    mock_save_training_state.assert_called_once()
+
+
+def test_save_training_state(tmp_path, optimizer, scheduler):
+    save_training_state(tmp_path, 10, optimizer, scheduler)
+    assert (tmp_path / TRAINING_STATE_DIR).is_dir()
+    assert (tmp_path / TRAINING_STATE_DIR / TRAINING_STEP).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / RNG_STATE).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_STATE).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_PARAM_GROUPS).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / SCHEDULER_STATE).is_file()
+
+
+def test_save_load_training_state(tmp_path, optimizer, scheduler):
+    save_training_state(tmp_path, 10, optimizer, scheduler)
+    loaded_step, loaded_optimizer, loaded_scheduler = load_training_state(tmp_path, optimizer, scheduler)
+    assert loaded_step == 10
+    assert loaded_optimizer is optimizer
+    assert loaded_scheduler is scheduler
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 071e17b8..b2f14694 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,8 +1,3 @@
-import random
-from typing import Callable
-
-import numpy as np
-import pytest
 import torch
 from datasets import Dataset
 
@@ -10,50 +5,6 @@ from lerobot.common.datasets.push_dataset_to_hub.utils import calculate_episode_
 from lerobot.common.datasets.utils import (
     hf_transform_to_torch,
 )
-from lerobot.common.utils.utils import (
-    get_global_random_state,
-    seeded_context,
-    set_global_random_state,
-    set_global_seed,
-)
-
-# Random generation functions for testing the seeding and random state get/set.
-rand_fns = [
-    random.random,
-    np.random.random,
-    lambda: torch.rand(1).item(),
-]
-if torch.cuda.is_available():
-    rand_fns.append(lambda: torch.rand(1, device="cuda"))
-
-
-@pytest.mark.parametrize("rand_fn", rand_fns)
-def test_seeding(rand_fn: Callable[[], int]):
-    set_global_seed(0)
-    a = rand_fn()
-    with seeded_context(1337):
-        c = rand_fn()
-    b = rand_fn()
-    set_global_seed(0)
-    a_ = rand_fn()
-    b_ = rand_fn()
-    # Check that `set_global_seed` lets us reproduce a and b.
-    assert a_ == a
-    # Additionally, check that the `seeded_context` didn't interrupt the global RNG.
-    assert b_ == b
-    set_global_seed(1337)
-    c_ = rand_fn()
-    # Check that `seeded_context` and `global_seed` give the same reproducibility.
-    assert c_ == c
-
-
-def test_get_set_random_state():
-    """Check that getting the random state, then setting it results in the same random number generation."""
-    random_state_dict = get_global_random_state()
-    rand_numbers = [rand_fn() for rand_fn in rand_fns]
-    set_global_random_state(random_state_dict)
-    rand_numbers_ = [rand_fn() for rand_fn in rand_fns]
-    assert rand_numbers_ == rand_numbers
 
 
 def test_calculate_episode_data_index():