diff --git a/src/lerobot/configs/train.py b/src/lerobot/configs/train.py
index ab34d43c7..11bdb480a 100644
--- a/src/lerobot/configs/train.py
+++ b/src/lerobot/configs/train.py
@@ -99,12 +99,13 @@ class TrainPipelineConfig(HubMixin):
     batch_size: int = 8
     prefetch_factor: int = 4
     persistent_workers: bool = True
-    # Use a deterministic O(1)-memory sampler (seeded Feistel permutation) instead of materialized
+    # Use a deterministic O(1)-memory sampler (seeded Feistel permutation) instead of RNG-based
     # index shuffling. The data order becomes a pure function of (seed, epoch), which makes
     # distributed sharding immune to RNG desync, keeps sampler memory constant in dataset size,
     # and enables sample-exact resume of interrupted runs. The shuffle is pseudo-random rather
-    # than a true uniform permutation. Not compatible with dataset.streaming.
-    deterministic_sampler: bool = False
+    # than a true uniform permutation. Set to false to restore the legacy RNG-based shuffle
+    # order. Ignored when dataset.streaming is enabled.
+    deterministic_sampler: bool = True
     steps: int = 100_000
     eval_freq: int = 20_000
     log_freq: int = 200
@@ -139,12 +140,6 @@ class TrainPipelineConfig(HubMixin):
         return self.policy  # type: ignore[return-value]
 
     def validate(self) -> None:
-        if self.deterministic_sampler and self.dataset.streaming:
-            raise ValueError(
-                "deterministic_sampler requires a map-style dataset and is not compatible with "
-                "dataset.streaming=true."
-            )
-
         # HACK: We parse again the cli args here to get the pretrained paths if there was some.
         policy_path = parser.get_path_arg("policy")
         reward_model_path = parser.get_path_arg("reward_model")
diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 405928d45..186431c54 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -387,7 +387,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
 
     # create dataloader for offline training
-    if cfg.deterministic_sampler:
+    if cfg.deterministic_sampler and not cfg.dataset.streaming:
         # Data order is a pure function of (seed, epoch): nothing to synchronize across ranks,
         # O(1) memory in dataset size, and a resumed run continues at the exact sample where the
         # checkpoint left off (up to accelerate's even_batches padding at epoch boundaries).