diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 2cee5372a..a4fede3d3 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -211,30 +211,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             logging.info("Creating env")
         eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
 
-    # Create processors first - for SARM/ReWiND, this updates config.num_stages from dataset annotations
-    # This must happen BEFORE make_policy() so the model is created with the correct number of stages
-    processor_kwargs = {}
-    postprocessor_kwargs = {}
-    if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
-        # Only provide dataset_stats when not resuming from saved processor state
-        processor_kwargs["dataset_stats"] = dataset.meta.stats
-    
-    # For ReWiND and SARM, always provide dataset_meta for progress normalization
-    if cfg.policy.type in ["rewind", "sarm"]:
-        processor_kwargs["dataset_meta"] = dataset.meta
-
-    # For pretrained paths, we need to defer some overrides until after policy creation
-    # But for SARM/ReWiND, we need processor to run first to update num_stages
-    if cfg.policy.pretrained_path is None or cfg.policy.type in ["rewind", "sarm"]:
-        if is_main_process:
-            logging.info("Creating processors")
-        preprocessor, postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path if cfg.policy.type not in ["rewind", "sarm"] else None,
-            **processor_kwargs,
-            **postprocessor_kwargs,
-        )
-
     if is_main_process:
         logging.info("Creating policy")
     policy = make_policy(
@@ -246,8 +222,18 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     # Wait for all processes to finish policy creation before continuing
     accelerator.wait_for_everyone()
 
-    # For pretrained paths (non-SARM/ReWiND), create processors now with policy-dependent overrides
-    if cfg.policy.pretrained_path is not None and cfg.policy.type not in ["rewind", "sarm"]:
+    # Create processors - only provide dataset_stats if not resuming from saved processors
+    processor_kwargs = {}
+    postprocessor_kwargs = {}
+    if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
+        # Only provide dataset_stats when not resuming from saved processor state
+        processor_kwargs["dataset_stats"] = dataset.meta.stats
+    
+    # For ReWiND and SARM, always provide dataset_meta for progress normalization
+    if cfg.policy.type in ["rewind", "sarm"]:
+        processor_kwargs["dataset_meta"] = dataset.meta
+
+    if cfg.policy.pretrained_path is not None:
         processor_kwargs["preprocessor_overrides"] = {
             "device_processor": {"device": device.type},
             "normalizer_processor": {
@@ -267,12 +253,12 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             },
         }
 
-        preprocessor, postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path,
-            **processor_kwargs,
-            **postprocessor_kwargs,
-        )
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        pretrained_path=cfg.policy.pretrained_path,
+        **processor_kwargs,
+        **postprocessor_kwargs,
+    )
 
     if is_main_process:
         logging.info("Creating optimizer and scheduler")