diff --git a/lerobot/common/envs/configs.py b/lerobot/common/envs/configs.py
index 06415764..66edde6b 100644
--- a/lerobot/common/envs/configs.py
+++ b/lerobot/common/envs/configs.py
@@ -224,6 +224,9 @@ class HILSerlRobotEnvConfig(EnvConfig):
     push_to_hub: bool = True
     pretrained_policy_name_or_path: Optional[str] = None
     reward_classifier_pretrained_path: Optional[str] = None
+    number_of_steps_after_success: int = (
+        0  # For the reward classifier, to record more positive examples after a success
+    )
 
     def gym_kwargs(self) -> dict:
         return {}
diff --git a/lerobot/scripts/server/gym_manipulator.py b/lerobot/scripts/server/gym_manipulator.py
index 7232b6f3..2b221d71 100644
--- a/lerobot/scripts/server/gym_manipulator.py
+++ b/lerobot/scripts/server/gym_manipulator.py
@@ -1923,7 +1923,7 @@ def init_reward_classifier(cfg):
 ###########################################################
 
 
-def record_dataset(env, policy, cfg, success_collection_steps=0):
+def record_dataset(env, policy, cfg):
     """
     Record a dataset of robot interactions using either a policy or teleop.
 
@@ -1940,7 +1940,7 @@ def record_dataset(env, policy, cfg, success_collection_steps=0):
             - fps: Frames per second for recording
             - push_to_hub: Whether to push dataset to Hugging Face Hub
             - task: Name/description of the task being recorded
-        success_collection_steps: Number of additional steps to continue recording after
+            - number_of_steps_after_success: Number of additional steps to continue recording after
                                   a success (reward=1) is detected. This helps collect
                                   more positive examples for reward classifier training.
     """
@@ -2047,7 +2047,7 @@ def record_dataset(env, policy, cfg, success_collection_steps=0):
             really_done = terminated or truncated
             if success_detected:
                 success_steps_collected += 1
-                really_done = success_steps_collected >= success_collection_steps
+                really_done = success_steps_collected >= cfg.number_of_steps_after_success
 
             frame["next.done"] = np.array([really_done], dtype=bool)
             frame["task"] = cfg.task
@@ -2065,7 +2065,7 @@ def record_dataset(env, policy, cfg, success_collection_steps=0):
             if (terminated or truncated) and not success_detected:
                 # Regular termination without success
                 break
-            elif success_detected and success_steps_collected >= success_collection_steps:
+            elif success_detected and success_steps_collected >= cfg.number_of_steps_after_success:
                 # We've collected enough success states
                 logging.info(f"Collected {success_steps_collected} additional success states")
                 break
@@ -2139,12 +2139,10 @@ def main(cfg: EnvConfig):
             policy.to(cfg.device)
             policy.eval()
 
-        # Get success_collection_steps from config or default to 15
         record_dataset(
             env,
             policy=policy,
             cfg=cfg,
-            success_collection_steps=0,
         )
         exit()