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About this paper
Changle Qu, Sunhao Dai, Hengyi Cai, Yuqi Zhou, Xinran Chen, Simon, Jun Xu
The TurnSight framework enhances tool-integrated reasoning in large language models by introducing a turn-level hindsight self-distillation approach that leverages the model's own execution outcomes to provide state-aligned, fine-grained credit assignment. This method achieved a 7.7% improvement in overall average performance across benchmarks, demonstrating strong generalization to out-of-domain tasks.
Key insights
- State-aligned feedback, derived from an agent's own execution outcomes, provides more effective and relevant supervision for dynamic multi-turn tool use than global or misaligned privileged contexts.
- Aggregating hindsight evidence at the turn level ensures semantic coherence for complete tool interactions, overcoming the limitations of conflicting token-level signals.
- Employing a multi-lookahead teacher selection with directional agreement reliably identifies informative hindsight signals from varying temporal perspectives.
Summary by alphaXiv.
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