diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 3186eae44..f88d5c02f 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -303,29 +303,25 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             device=device,
         )
 
-    # Compute per-timestep normalizer for relative actions (main process computes, others load)
+    # Compute per-timestep normalizer for relative actions
+    # Each process computes stats independently to avoid distributed sync issues
     relative_normalizer = None
     if cfg.use_relative_actions:
-        stats_path = cfg.output_dir / "relative_stats.pt"
         mode = "actions + state" if cfg.use_relative_state else "actions only"
-        
         if is_main_process:
             logging.info(colored(f"Relative mode: {mode}", "cyan", attrs=["bold"]))
             logging.info("Computing per-timestep stats from dataset (first 1000 batches)...")
+        
+        temp_loader = torch.utils.data.DataLoader(
+            dataset, batch_size=cfg.batch_size, shuffle=False, num_workers=0
+        )
+        mean, std = compute_relative_action_stats(temp_loader, num_batches=1000)
+        relative_normalizer = PerTimestepNormalizer(mean, std)
+        
+        if is_main_process:
             cfg.output_dir.mkdir(parents=True, exist_ok=True)
-            temp_loader = torch.utils.data.DataLoader(
-                dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=0
-            )
-            mean, std = compute_relative_action_stats(temp_loader, num_batches=1000)
-            relative_normalizer = PerTimestepNormalizer(mean, std)
-            relative_normalizer.save(stats_path)
-            logging.info(f"Saved stats to: {stats_path}")
-        
-        # Barrier: wait for main process to finish computing and saving stats
-        accelerator.wait_for_everyone()
-        
-        if not is_main_process:
-            relative_normalizer = PerTimestepNormalizer.load(stats_path)
+            relative_normalizer.save(cfg.output_dir / "relative_stats.pt")
+            logging.info(f"Saved stats to: {cfg.output_dir / 'relative_stats.pt'}")
 
     step = 0  # number of policy updates (forward + backward + optim)