ChainPrune: Evaluating and Reducing Redundancy in Long Chain-of-Thought Reasoning
Chain-of-Thought (CoT) reasoning has significantly enhanced the multi-step problem-solving capabilities of large language models (LLMs) by introducing explicit intermediate reasoning. However, advanced Large Reasoning Models (LRMs) often exhibit overthinking behaviors, including excessively long reasoning steps, redundant steps, and high computational overhead. Existing token-length reward strategies aim to promote concise outputs, but often result in pseudo-conciseness, where token count is reduced, yet redundant reasoning persists, leading to longer and less structurally efficient chains. To address these limitations, we propose ChainPrune, a novel reasoning path semantic structural opt...
| Authors | Weihang Pan, Zhengxu Yu, Yuxiang Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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