diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 405d619c8..dd36f4a39 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -13,6 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 import logging
 import time
 from contextlib import nullcontext
@@ -304,29 +305,27 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         )
 
     # Compute per-timestep normalizer for relative actions
-    # Only main process computes, then broadcasts to avoid video decoder issues
+    # Compute BEFORE accelerator.prepare() to avoid NCCL timeouts during long computation
     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)
-            del temp_loader
+        
+        # All ranks compute independently to avoid NCCL timeout (computation takes ~10min)
+        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
+        gc.collect()  # Clean up any video decoder resources
+        relative_normalizer = PerTimestepNormalizer(mean, std)
+        
+        if is_main_process:
             cfg.output_dir.mkdir(parents=True, exist_ok=True)
-            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"])
+            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)