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>
Pepijn