diff --git a/scripts/visualize_sarm_predictions.py b/scripts/visualize_sarm_predictions.py
index 8c9d7ea23..1841af63c 100644
--- a/scripts/visualize_sarm_predictions.py
+++ b/scripts/visualize_sarm_predictions.py
@@ -266,9 +266,10 @@ def run_inference(
     
     for current_frame in tqdm(range(len(video_embeddings)), desc="Creating slices"):
         # Compute frame indices using SARM pattern:
-        # [initial_frame, t-(7*gap), t-(6*gap), ..., t-gap, t]
-        deltas = model.config.observation_delta_indices(current_frame)
-        frame_indices = [max(0, current_frame + delta) for delta in deltas]
+        # [initial_frame (0), t-(7*gap), t-(6*gap), ..., t-gap, t]
+        # The first delta is -100000 which clamps to 0 (episode start)
+        deltas = model.config.observation_delta_indices
+        frame_indices = [max(0, min(current_frame + delta, len(video_embeddings) - 1)) for delta in deltas]
         
         # Extract slice
         video_slice = video_embeddings[frame_indices]
diff --git a/src/lerobot/policies/sarm/configuration_sarm.py b/src/lerobot/policies/sarm/configuration_sarm.py
index bccac50b5..a89e367e8 100644
--- a/src/lerobot/policies/sarm/configuration_sarm.py
+++ b/src/lerobot/policies/sarm/configuration_sarm.py
@@ -129,25 +129,24 @@ class SARMConfig(PreTrainedConfig):
         """Validate input and output features."""
         pass
     
-    def observation_delta_indices(self, episode_frame_index: int) -> list[int]:
-        """Compute delta indices for SARM temporal sampling.
+    @property
+    def observation_delta_indices(self) -> list[int]:
+        """Load frames for SARM temporal sampling.
         
         Per SARM paper (Section A.4), the model uses 9 frames:
-        - Frame 0: Initial frame of the episode (delta = -episode_frame_index)
+        - Frame 0: Initial frame of the episode
         - Frames 1-8: 8 consecutive frames with frame_gap spacing ending at current frame
         
-        The dataloader converts these to seconds: delta_seconds = delta / fps
-        This means the first delta (-episode_frame_index) becomes -current_time,
-        which correctly points to t=0 (the initial frame).
+        The first delta uses a large negative offset (-1_000_000) that will be clamped
+        to the episode start (frame 0) by the dataset loader. This ensures we always
+        get the initial frame regardless of the current position in the episode.
+        
         
-        Args:
-            episode_frame_index: Current frame index within the episode (0, 1, 2, ...)
-            
         Returns:
-            9 delta indices: [-episode_frame_index, -(7*gap), -(6*gap), ..., -gap, 0]
+            9 delta indices: [-1_000_000, -(7*gap), -(6*gap), ..., -gap, 0]
         """
-        # First delta: negative of current frame index to reach frame 0
-        initial_frame_delta = -episode_frame_index
+        # First delta: large negative to always clamp to episode start (frame 0)
+        initial_frame_delta = -1_000_000
         
         # Remaining 8 deltas: consecutive frames with frame_gap spacing
         num_consecutive = self.num_frames - 1  # 8 frames