TurnWise applies turn-level credit assignment to streaming video RL

A paper on arXiv describes TurnWise, a reinforcement learning framework for streaming video understanding, where a model repeatedly decides whether to answer or keep observing. The authors note that existing GRPO-based approaches keep a single trajectory-level advantage across turns, which they say can reinforce redundant replies after earlier successes and discourage useful follow-ups after failures. TurnWise instead assigns credit per turn, using silence-completed prefix scoring to build group-relative advantages from each decision's remaining return. It also averages token losses within each turn before averaging across turns, so responding and waiting carry equal weight regardless of output length. The authors report it outperforms prior RL approaches in their experiments without a learned critic or extra rollouts.