diff --git a/Makefile b/Makefile
index a0163f94..c561deb0 100644
--- a/Makefile
+++ b/Makefile
@@ -20,6 +20,8 @@ build-gpu:
 test-end-to-end:
 	${MAKE} test-act-ete-train
 	${MAKE} test-act-ete-eval
+	${MAKE} test-act-ete-train-amp
+	${MAKE} test-act-ete-eval-amp
 	${MAKE} test-diffusion-ete-train
 	${MAKE} test-diffusion-ete-eval
 	${MAKE} test-tdmpc-ete-train
@@ -29,6 +31,7 @@ test-end-to-end:
 test-act-ete-train:
 	python lerobot/scripts/train.py \
 		policy=act \
+		policy.dim_model=64 \
 		env=aloha \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -51,9 +54,40 @@ test-act-ete-eval:
 		env.episode_length=8 \
 		device=cpu \
 
+test-act-ete-train-amp:
+	python lerobot/scripts/train.py \
+		policy=act \
+		policy.dim_model=64 \
+		env=aloha \
+		wandb.enable=False \
+		training.offline_steps=2 \
+		training.online_steps=0 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		device=cpu \
+		training.save_model=true \
+		training.save_freq=2 \
+		policy.n_action_steps=20 \
+		policy.chunk_size=20 \
+		training.batch_size=2 \
+		hydra.run.dir=tests/outputs/act/ \
+		use_amp=true
+
+test-act-ete-eval-amp:
+	python lerobot/scripts/eval.py \
+		-p tests/outputs/act/checkpoints/000002 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		env.episode_length=8 \
+		device=cpu \
+		use_amp=true
+
 test-diffusion-ete-train:
 	python lerobot/scripts/train.py \
 		policy=diffusion \
+		policy.down_dims=\[64,128,256\] \
+		policy.diffusion_step_embed_dim=32 \
+		policy.num_inference_steps=10 \
 		env=pusht \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -100,7 +134,6 @@ test-tdmpc-ete-eval:
 		env.episode_length=8 \
 		device=cpu \
 
-
 test-default-ete-eval:
 	python lerobot/scripts/eval.py \
 		--config lerobot/configs/default.yaml \
diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py
index eea3b650..83159465 100644
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -100,7 +100,7 @@ def update_policy(
     use_amp: bool = False,
 ):
     """Returns a dictionary of items for logging."""
-    start_time = time.time()
+    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():
@@ -137,7 +137,7 @@ def update_policy(
         "loss": loss.item(),
         "grad_norm": float(grad_norm),
         "lr": optimizer.param_groups[0]["lr"],
-        "update_s": time.time() - start_time,
+        "update_s": time.perf_counter() - start_time,
         **{k: v for k, v in output_dict.items() if k != "loss"},
     }