diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py
index ef2d019b3..d3e0caa2d 100644
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -24,13 +24,11 @@ from lerobot.common.utils.utils import (
 from lerobot.scripts.eval import eval_policy
 
 
-def update_policy(policy, batch, optimizer, lr_scheduler=None):
+def update_policy(cfg, policy, batch, optimizer, lr_scheduler=None):
     start_time = time.time()
 
-    # Diffusion
-    policy.diffusion.train()
-    # Act
-    policy.train()
+    model = policy.diffusion if hasattr(policy, "diffusion") else policy  # TODO: hacky, remove this line
+    model.train()
 
     batch = policy.normalize_inputs(batch)
 
@@ -43,7 +41,7 @@ def update_policy(policy, batch, optimizer, lr_scheduler=None):
     model = policy.diffusion if hasattr(policy, "diffusion") else policy  # TODO: hacky, remove this line
     grad_norm = torch.nn.utils.clip_grad_norm_(
         model.parameters(),
-        policy.cfg.grad_clip_norm,
+        cfg.grad_clip_norm,
         error_if_nonfinite=False,
     )
 
@@ -58,7 +56,7 @@ def update_policy(policy, batch, optimizer, lr_scheduler=None):
     info = {
         "loss": loss.item(),
         "grad_norm": float(grad_norm),
-        "lr": policy.cfg.lr if lr_scheduler is None else lr_scheduler.get_last_lr()[0],
+        "lr": optimizer.param_groups[0]['lr'],
         "update_s": time.time() - start_time,
     }
 
@@ -283,10 +281,10 @@ def train(cfg: dict, out_dir=None, job_name=None):
             {"params": [p for n, p in policy.named_parameters() if not n.startswith("backbone") and p.requires_grad]},
             {
                 "params": [p for n, p in policy.named_parameters() if n.startswith("backbone") and p.requires_grad],
-                "lr": policy.cfg.lr_backbone,
+                "lr": cfg.lr_backbone,
             },
         ]
-        optimizer = torch.optim.AdamW(optimizer_params_dicts, lr=policy.cfg.lr, weight_decay=policy.cfg.weight_decay)
+        optimizer = torch.optim.AdamW(optimizer_params_dicts, lr=cfg.lr, weight_decay=cfg.weight_decay)
         lr_scheduler = None
     elif isinstance(policy, DiffusionPolicy):
         optimizer = torch.optim.Adam(
@@ -364,7 +362,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
         for key in batch:
             batch[key] = batch[key].to(cfg.device, non_blocking=True)
 
-        train_info = update_policy(policy, batch, optimizer, lr_scheduler)
+        train_info = update_policy(cfg, policy, batch, optimizer, lr_scheduler)
 
         # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
         if step % cfg.log_freq == 0: