TurnSight 把工具推理的强化学习拆到回合级，用事后视角筛信号，三个基准上都有效，代码开源了。
TurnSight 是一个面向工具集成推理（TIR）的回合级事后自蒸馏框架。TurnSight 不从轨迹级或令牌级反馈学习，而是基于执行条件的事后状态生成监督信号。TurnSight 构造多个不同前瞻视野的事后视图，并通过跨视野方向一致性筛选可靠信号。论文在三个基准上验证了效果，代码已开源在 GitHub。
TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning
Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, existing reinforcement learning methods often rely on trajectory-level supervision, limiting fine-grained credit assignment in long-horizon TIR scenarios. On-policy self-distillation offers denser signals through teacher branches with privileged context, but existing approaches typically derive such context from ground-truth answers or retrieved skills, which may not reflect the states actually visited by the agent. Moreover, token-level supervision fails to capture the turn-level structure of tool interactions. To address this, we propose TurnSight, a turn-level hindsight self-distillation framework that derives supervision directly from execution-conditioned hindsight. It then constructs multiple hindsight views with different lookahead horizons and selects reliable supervision through cross-horizon directional agreement. Finally, the selected hindsight signal is normalized across sibling rollouts and used to adaptively modulate RL advantages while preserving their original optimization direction. Extensive experiments on three benchmarks demonstrate the effectiveness of TurnSight. Our codes are available at https://github.com/quchangle1/TurnSight.