TurnWise targets credit errors in streaming video

TurnWise applies turn-level Group Relative Policy Optimization (GRPO) to streaming video, where a model repeatedly chooses to answer or wait as new frames arrive. Its central claim is specific: silence-completed prefix scoring estimates each decision’s group-relative remaining return, so a later reply is not trained using rewards earned before that decision.

The problem is that standard trajectory-level GRPO broadcasts one advantage across every turn. A redundant reply after an early success can inherit positive credit, while a useful follow-up after earlier failures can inherit negative credit. TurnWise separates the return from each turn onward from the reward already accrued, then weights response and waiting turns equally in the policy loss.

The method is evaluated on ProactiveVQA, OVO-Timing, and StreamingBench using Qwen2.5-VL-3B and Qwen3-VL-4B backbones.…