SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning 事件

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning arXiv:2605.30832v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{overthinking}), incurring high computational overhead without improving answer correctness. Existing mitigation strategies typically rely on token-unifo