diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 64d0884ea..5eeef8a8f 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -172,16 +172,13 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     # We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting the lr_scheduler steps based on the num_processes
     # We set find_unused_parameters=True to handle models with conditional computation
     if accelerator is None:
-        from datetime import timedelta
-
-        from accelerate.utils import DistributedDataParallelKwargs, InitProcessGroupKwargs
+        from accelerate.utils import DistributedDataParallelKwargs
 
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
-        init_kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=int(os.environ.get("NCCL_TIMEOUT", 600))))
         force_cpu = cfg.policy.device == "cpu"
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
-            kwargs_handlers=[ddp_kwargs, init_kwargs],
+            kwargs_handlers=[ddp_kwargs],
             cpu=force_cpu,
         )
 
@@ -226,7 +223,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             chunk_size = cfg.policy.chunk_size
             hf = dataset.hf_dataset
             total_frames = len(hf)
-            max_samples = min(500_000, total_frames - chunk_size)
+            max_samples = min(100_000, total_frames - chunk_size)
             indices = np.random.choice(total_frames - chunk_size, max_samples, replace=False)
             logging.info(
                 f"use_delta_actions is enabled — computing delta action stats "