SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning

• URL: http://arxiv.org/abs/2605.30832v1
• Date: Fri, 29 May 2026 04:37:49 GMT
• Title: SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
• Abstract summary: We propose textscSLAT (Segment-Level Adaptive Trimming), an RL framework that selectively suppresses redundant segments.<n> Empirical results indicate that textscSLAT establishes a superior accuracy-efficiency frontier, reducing reasoning length by $50%$.<n>Overall, our results suggest that theoretically grounded, segment-aware trimming is a promising direction for efficient CoT reasoning in large language models.
• Score: 26.883032881561423
• License: http://creativecommons.org/licenses/by/4.0/
• 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-uniform length penalties, which provide coarse, segment-agnostic pressure toward shorter outputs and can inadvertently suppress useful reasoning alongside redundancy. To address this, we demonstrate that inefficiency concentrates in high-probability segments with low marginal utility. We derive a theoretical characterization of segment suboptimality under the correctness-length trade-off objective and propose \textsc{SLAT} (Segment-Level Adaptive Trimming), an RL framework that selectively suppresses redundant segments based on this criterion. Empirical results on standard benchmarks indicate that \textsc{SLAT} establishes a superior accuracy-efficiency Pareto frontier, reducing reasoning length by $50\%$ relative to uncompressed baselines while maintaining competitive accuracy. Overall, our results suggest that theoretically grounded, segment-aware trimming is a promising direction for efficient CoT reasoning in large language models.

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