vla jepa obs stride and lingbot action noise prob

src/lerobot/policies/lingbot_va/configuration_lingbot_va.py

@@ -130,6 +130,12 @@ class LingBotVAConfig(PreTrainedConfig):
     # Cosine tightens the loss tail and often nudges final loss down; it does NOT reduce the
     # flow-matching estimator's step-to-step noise (that's metric variance, LR-independent).
     scheduler_type: str = "constant_with_warmup"
+    # Probability of corrupting the action stream's conditioning (clean/context) tokens with
+    # flow-matching noise during training, mirroring the video stream's noisy_cond_prob=0.5.
+    # Upstream train.py hardcodes 0.0 for actions (never corrupted) with no exposed knob; this is
+    # an experimental deviation to make the model more tolerant of imperfect action history
+    # (e.g. clamp-induced drift between predicted and executed actions during rollout).
+    action_noisy_cond_prob: float = 0.0
 
     def __post_init__(self):
         super().__post_init__()

src/lerobot/policies/lingbot_va/modeling_lingbot_va.py

@@ -311,7 +311,11 @@ class LingBotVAPolicy(PreTrainedPolicy):
             latents, self._train_sched_latent, action_mask=None, action_mode=False, noisy_cond_prob=0.5
         )
         action_dict = self._add_noise_stream(
-            actions, self._train_sched_action, action_mask=actions_mask, action_mode=True, noisy_cond_prob=0.0
+            actions,
+            self._train_sched_action,
+            action_mask=actions_mask,
+            action_mode=True,
+            noisy_cond_prob=self.config.action_noisy_cond_prob,
         )
         latent_dict["text_emb"] = text_emb
         action_dict["text_emb"] = text_emb

src/lerobot/policies/vla_jepa/configuration_vla_jepa.py

@@ -149,9 +149,14 @@ class VLAJEPAConfig(PreTrainedConfig):
 
     @property
     def observation_delta_indices(self) -> list[int]:
-        # load video_horizon frames starting from current timestep: [t, t+1, ..., t+video_horizon-1]
+        # matches original repo's observation_indices=list(range(video_horizon)) when the chunk
-        # matches original repo's observation_indices=list(range(video_horizon))
+        # fits within video_horizon frames. When chunk_size is longer (e.g. folding's 30-step
+        # chunk vs 8 video frames), spread the frames evenly across the chunk instead of
+        # clustering them at the start, so the world model sees dynamics over the whole horizon.
+        if self.num_video_frames >= self.chunk_size:
             return list(range(self.num_video_frames))
+        stride = (self.chunk_size - 1) // (self.num_video_frames - 1)
+        return [i * stride for i in range(self.num_video_frames)]
 
     @property
     def action_delta_indices(self) -> list[int]: