feat: oversample sparse VQA annotations (recipe consumption + weighted sampler)

VQA annotations are sparse, so VQA was badly underrepresented in training:
its effective share was weight x density, and blend draws that picked an
ask_vqa* sub-recipe for a non-VQA frame were wasted entirely.

Two pieces:

1. Recipe-side consumption (language_render.py): render_sample now routes
   any frame that carries a VQA annotation to a matching ask_vqa* sub-recipe,
   regardless of the weighted blend draw. No VQA annotation is wasted and no
   draw lands on a non-renderable VQA recipe — VQA's recipe-side share now
   equals the VQA-annotation density.

2. Dataset-side oversampling (WeightedEpisodeAwareSampler + vqa_target_fraction):
   a new weighted, episode-aware sampler draws frames with replacement by
   per-frame weight. When TrainPipelineConfig.vqa_target_fraction is set, the
   train script scans language_events, weights VQA frames so they make up
   ~that fraction of the training stream, and uses the weighted sampler. This
   is what actually lets VQA exceed its natural density. Default None keeps
   uniform episode-aware sampling unchanged.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lerobot/datasets/sampler.py

@@ -84,3 +84,66 @@ class EpisodeAwareSampler:
 
     def __len__(self) -> int:
         return len(self.indices)
+
+
+class WeightedEpisodeAwareSampler(EpisodeAwareSampler):
+    """``EpisodeAwareSampler`` that draws frames *with replacement* in
+    proportion to per-frame weights.
+
+    Used to oversample frames carrying a sparse annotation (e.g. a VQA
+    question) so the policy sees them more often than their natural
+    dataset density. One epoch still yields ``len(self.indices)``
+    samples — the weights only change the *composition* of the stream,
+    not its length. Each epoch re-draws, so the oversampled subset
+    varies run to run.
+    """
+
+    def __init__(
+        self,
+        dataset_from_indices: list[int],
+        dataset_to_indices: list[int],
+        frame_weights,
+        *,
+        episode_indices_to_use: list | None = None,
+        drop_n_first_frames: int = 0,
+        drop_n_last_frames: int = 0,
+    ):
+        """
+        Args:
+            dataset_from_indices: Episode start indices (see ``EpisodeAwareSampler``).
+            dataset_to_indices: Episode end indices.
+            frame_weights: 1-D sequence/tensor of non-negative weights, one per
+                dataset frame (length == total dataset frames). Higher weight ⇒
+                that frame is sampled more often.
+            episode_indices_to_use / drop_n_first_frames / drop_n_last_frames:
+                Same meaning as ``EpisodeAwareSampler`` — the episode-boundary
+                frame filtering is applied first, then weighting is restricted
+                to the surviving frames.
+        """
+        super().__init__(
+            dataset_from_indices,
+            dataset_to_indices,
+            episode_indices_to_use=episode_indices_to_use,
+            drop_n_first_frames=drop_n_first_frames,
+            drop_n_last_frames=drop_n_last_frames,
+            shuffle=False,
+        )
+        weights = torch.as_tensor(frame_weights, dtype=torch.double).flatten()
+        idx = torch.tensor(self.indices, dtype=torch.long)
+        if weights.numel() <= int(idx.max()):
+            raise ValueError(
+                f"frame_weights has {weights.numel()} entries but the sampler "
+                f"references frame index {int(idx.max())}."
+            )
+        selected = weights[idx]
+        if not torch.isfinite(selected).all() or bool((selected < 0).any()):
+            raise ValueError("frame_weights must be finite and non-negative.")
+        if float(selected.sum()) <= 0.0:
+            # All surviving frames have zero weight — fall back to uniform.
+            selected = torch.ones_like(selected)
+        self._weights = selected
+
+    def __iter__(self) -> Iterator[int]:
+        picks = torch.multinomial(self._weights, num_samples=len(self.indices), replacement=True)
+        for i in picks.tolist():
+            yield self.indices[i]