Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning
Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. In many current training setups, temporal supervision is applied through local objectives such as boundary regression or span generation, while verification is used mainly to rerank candidate segments at inference time. We study whether a frozen verifier can also guide training. Our multi-agent framework couples a trainable \emph{Grounder} with a frozen \emph{Verifier}: the Grounder samples candidate trajectories and evidence segments, the Verifier assigns query-conditioned segment scores, a group-relative policy-gradient objective favors traj...
| Authors | Mingwen Zhang, Jisheng Dang, Minqiang Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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