SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
Reducing Large Reasoning Model overhead by surgically removing redundant thought segments during inference.
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 ov...
ArXiv CS.AI
Source
Generated insight by OnAirToday AI
SLAT represents a critical shift from blunt token-length penalties to granular, segment-aware optimization for more efficient reasoning. By identifying and trimming redundant logic without sacrificing accuracy, this approach enables large language models to reach conclusions with significantly lower computational overhead.
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.