diff --git a/lerobot/common/logger.py b/lerobot/common/logger.py
index 8a374932..d8398ff1 100644
--- a/lerobot/common/logger.py
+++ b/lerobot/common/logger.py
@@ -18,6 +18,8 @@
 
 import logging
 import os
+import re
+from glob import glob
 from pathlib import Path
 
 import torch
@@ -73,7 +75,19 @@ class Logger:
             os.environ["WANDB_SILENT"] = "true"
             import wandb
 
+            wandb_run_id = None
+            if cfg.resume:
+                # Get the WandB run ID.
+                paths = glob(str(self._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]
+
             wandb.init(
+                id=wandb_run_id,
                 project=project,
                 entity=entity,
                 name=job_name,
@@ -87,14 +101,14 @@ class Logger:
                 # TODO(rcadene): split train and eval, and run async eval with job_type="eval"
                 job_type="train_eval",
                 # TODO(rcadene): add resume option
-                resume="must",
+                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
 
     @property
-    def last_checkpoint_path(self):
+    def last_checkpoint_path(self) -> Path:
         return self._last_checkpoint_path
 
     def save_model(self, policy: Policy, identifier: str):
@@ -112,6 +126,8 @@ class Logger:
                 )
                 artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
                 self._wandb.log_artifact(artifact)
+        if self._last_checkpoint_path.exists():
+            os.remove(self._last_checkpoint_path)
         os.symlink(save_dir.absolute(), self._last_checkpoint_path)  # TODO(now): Check this works
 
     def save_training_state(
diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py
index dc17f604..3713c9a7 100644
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -322,8 +322,8 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     logging.info("make_policy")
     policy = make_policy(
         hydra_cfg=cfg,
-        dataset_stats=offline_dataset.stats,
-        pretrained_policy_name_or_path=logger.last_checkpoint_path if cfg.resume else None,
+        dataset_stats=offline_dataset.stats if not cfg.resume else None,
+        pretrained_policy_name_or_path=str(logger.last_checkpoint_path) if cfg.resume else None,
     )
 
     # Create optimizer and scheduler
@@ -335,10 +335,12 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     if cfg.resume:
         print("You have set resume=True, indicating that you wish to resume a run.")
         # Make sure there is a checkpoint.
-        if not Path(logger.last_checkpoint_path).exists():
-            raise RuntimeError(f"You have set resume=True, but {logger.last_checkpoint_path} does not exist.")
+        if not logger.last_checkpoint_path.exists():
+            raise RuntimeError(
+                f"You have set resume=True, but {str(logger.last_checkpoint_path)} does not exist."
+            )
         # Get the configuration file from the last checkpoint.
-        checkpoint_cfg = init_hydra_config(logger.last_checkpoint_path)
+        checkpoint_cfg = init_hydra_config(str(logger.last_checkpoint_path))
         # TODO(now): Do a diff check.
         cfg = checkpoint_cfg
         step = logger.load_last_training_state(optimizer, lr_scheduler)
@@ -376,8 +378,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             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_model(
+            logger.save_checkpont(
+                step,
                 policy,
+                optimizer,
+                lr_scheduler,
                 identifier=str(step).zfill(
                     max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps)))
                 ),