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Source note2 min read

TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning

Source

arXiv

arxiv.org/abs/2608.04007v1

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Source

Plain-Language Brief

The authors propose a framework called TurnSight for Tool-Integrated Reasoning (TIR). This framework aims to improve fine-grained credit assignment in long-horizon TIR scenarios by addressing the limitations of existing reinforcement learning methods. The main issue is that these methods rely on trajectory-level supervision, and existing self-distillation approaches derive context from ground-truth answers or retrieved skills which may not reflect the actual states visited by the agent. TurnSight derives supervision directly from execution-conditioned hindsight and constructs multiple hindsight views to select reliable supervision. The selected signal is then used to adaptively modulate RL advantages while preserving their original optimization direction.

What the Document Says

  • Tool-Integrated Reasoning (TIR) helps LLMs solve complex tasks through tool interactions.
  • Existing reinforcement learning methods for TIR often rely on trajectory-level supervision, which limits fine-grained credit assignment in long-horizon scenarios.
  • On-policy self-distillation provides denser signals but derives context from ground-truth answers or retrieved skills.
  • Token-level supervision fails to capture the turn-level structure of tool interactions.
  • TurnSight is a framework for TIR that addresses these issues by deriving supervision directly from execution-conditioned hindsight and constructing multiple hindsight views.
  • It selects reliable supervision through cross-horizon directional agreement and adapts RL advantages with the selected signal.
  • The authors claim that TurnSight is effective based on extensive experiments on three benchmarks.

Key Concepts Mentioned

  • Tool-Integrated Reasoning (TIR)
  • Reinforcement Learning (RL)
  • On-policy self-distillation
  • Hindsight Self-Distillation
  • Execution-conditioned hindsight
  • TurnSight

Claims Made by the Document

  • Existing reinforcement learning methods for TIR have limitations in fine-grained credit assignment.
  • TurnSight addresses these limitations by deriving supervision directly from execution-conditioned hindsight and constructing multiple hindsight views.
  • TurnSight improves the effectiveness of TIR based on extensive experiments.

Evidence Used

  • The text describes the limitations of existing reinforcement learning methods for TIR
  • The text explains how TurnSight derives supervision and constructs multiple hindsight views to address these limitations.
  • The text mentions extensive experiments that demonstrate the effectiveness of TurnSight.

Methods / Approach

  • Turn-level hindsight self-distillation
  • Cross-horizon directional agreement
  • Adaptive modulation of RL advantages

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.