Less from More: Reinforcing Sparse Video Reasoning from Dense References

The paper introduces SAVER, a dense-to-sparse post‑training framework that leverages dense video views as training‑time references to improve sparse‑frame inference. By optimizing paired dense and sparse views with grounding and reliability‑gated reference rewards, SAVER learns to preserve task‑relevant temporal evidence while using far fewer frames. Evaluated on three temporal grounding and six video question‑answering benchmarks, SAVER consistently outperforms dense‑frame baselines, matching or surpassing them with substantially reduced frame budgets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

• llms
• nlp
• benchmarks