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ARXIV:2605.30832 · LLM REASONING · SUBMITTED 01 JUN · 20:23 UTC · FRESHNESS STALE
ARXIV:2605.30832LLM REASONINGSUBMITTED 01 JUN · 20:23 UTCFRESHNESS STALEJian Yao · Xiongcai Luo · Ran Cheng · Kay Chen Tan · arXiv
A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs.
Opportunity summary
Pain A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs. However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{overthinking}), incurring high computational…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this, we demonstrate that inefficiency concentrates in high-probability segments with low marginal utility. Code availability is flagged in the production record; the…
LLM Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs.
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10.48550/arXiv.2605.30832A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs.
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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PROBLEM
A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs. However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{ov...
METHOD
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 o...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this, we demonstrate that inefficiency concentrates in high-probability segments with low marginal utility. Code availability is flagged in the production record; the public repository link sti...
WHY NOW
LLM Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 19, "author": "Jian Yao; Xiongcai Luo; Ran Cheng; Kay Chen Tan", "title": "SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning", "creation date": null
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A segment-level adaptive trimming framework for efficient Chain-of-Thought reasoning in LLMs that selectively suppresses redundant segments to improve accuracy-efficiency trade-offs.
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LLM Reasoning
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