TurnWise 框架通过回合级信用分配提升流媒体视频理解能力
Turns Matter: Advancing Streaming Video RL via Turn-Level Credit
TurnWise 框架通过回合级信用分配和决策权重平衡改进流媒体视频理解，其回合级信用分配使用沉默完成的前缀评分构建组相对优势，回合均值策略优化平均每个回合内的 token 损失，实验表明该方法优于现有流媒体视频理解 RL 方法。
Published on Nov 4
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Abstract
TurnWise improves streaming video understanding by assigning turn-level credit and balancing decision weights in group relative policy optimization.
Streaming video understanding requires models to repeatedly decide whether to respond or continue observing. Recently, Group Relative Policy Optimization (GRPO) has been applied to this setting, with existing approaches emphasizing reward design while retaining a single trajectory-level advantage across turns. Such shared credit can reinforce redundant replies after earlier successes and discourage useful follow-ups after earlier failures. We propose TurnWise, a turn-level GRPO framework that aligns learning with individual streaming decisions. TurnWise addresses the mismatch between trajectory-level feedback and turn-level interaction, promoting informative responses and reducing redundancy. Specifically, turn-level credit assignment uses silence-completed prefix scoring to construct group-relative advantages from each decision's remaining return, separating its learning signal from earlier rewards. Turn-mean policy optimization averages token losses within each turn and then across turns, giving response and waiting decisions equal weight regardless of output length. These complementary designs improve streaming policy learning without a learned critic or additional rollouts. Extensive experiments show our method outperforms prior RL approaches for streaming video understanding.
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