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refactor(g05): keep recipe runtime unchanged
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@@ -149,10 +149,9 @@ lerobot-train \
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The bundled `g05_bbox_subtask.yaml` recipe resolves the active
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`language_persistent` `subtask` and camera-scoped grounded `vqa` event at each
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sample timestamp. It applies independent `0.5` dropout to the optional BBox and
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Subtask targets, producing Action-only, Subtask+Action, BBox+Action, and joint
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BBox+Subtask+Action samples. An unavailable annotation is skipped before
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dropout through the recipe's `if_present` guard.
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sample timestamp. It emits each optional BBox or Subtask target when that
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annotation is present; unavailable annotations are skipped through the recipe's
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existing `if_present` guard.
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Grounded VQA boxes are converted from pixel-space `xyxy` JSON using the source
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camera dimensions captured before image resizing, then serialized as G0.5
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@@ -146,24 +146,6 @@ The renderer does not apply a tokenizer chat template. Policy processors decide
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Blend recipes select one weighted sub-recipe deterministically from the sample index.
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`recipes/subtask_mem.yaml` trains the compact core blend — high-level subtask prediction, low-level execution, and memory. `recipes/subtask_mem_vqa_speech.yaml` is the fuller variant that also adds VQA and spoken interjection responses.
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Message turns can set `dropout` to a probability between `0` and `1`. Dropout is
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deterministic for a given sample index and independent between turns. Combine it
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with `if_present` to sample optional supervision formats while gracefully
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skipping annotations that are unavailable:
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```yaml
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messages:
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- { role: user, content: "${task}", stream: low_level }
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- {
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role: assistant,
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content: "${subtask}",
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stream: low_level,
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target: true,
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if_present: subtask,
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dropout: 0.5,
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}
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```
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A message recipe with a supervised assistant turn on the `low_level` stream trains
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the π0.5 paper's joint sequence instead of a blend: the target span gets text CE
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while also conditioning the action losses in the same forward.
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