diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index f88d5c02f..405d619c8 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -304,24 +304,29 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         )
 
     # Compute per-timestep normalizer for relative actions
-    # Each process computes stats independently to avoid distributed sync issues
+    # Only main process computes, then broadcasts to avoid video decoder issues
     relative_normalizer = None
     if cfg.use_relative_actions:
         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:
+            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)
+            del temp_loader
             cfg.output_dir.mkdir(parents=True, exist_ok=True)
-            relative_normalizer.save(cfg.output_dir / "relative_stats.pt")
-            logging.info(f"Saved stats to: {cfg.output_dir / 'relative_stats.pt'}")
+            stats_path = cfg.output_dir / "relative_stats.pt"
+            torch.save({"mean": mean, "std": std}, stats_path)
+            logging.info(f"Saved stats to: {stats_path}")
+        
+        accelerator.wait_for_everyone()
+        
+        # All ranks load from saved file
+        stats_path = cfg.output_dir / "relative_stats.pt"
+        data = torch.load(stats_path, weights_only=True, map_location="cpu")
+        relative_normalizer = PerTimestepNormalizer(data["mean"], data["std"])
 
     step = 0  # number of policy updates (forward + backward + optim)