EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation

• URL: http://arxiv.org/abs/2601.03769v1
• Date: Wed, 07 Jan 2026 10:02:27 GMT
• Title: EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation
• Authors: Zihang Li, Yuhang Wang, Yikun Zong, Wenhan Yu, Xiaokun Yuan, Runhan Jiang, Zirui Liu, Tong Yang, Arthur Jiang,
• Abstract summary: Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models.<n>Existing fine-tuning datasets frequently suffer from the "answer right but reasoning wrong" probelm.<n>This paper proposes EntroCoT, a unified framework for automatically identifying and refining low-quality CoT supervision traces.
• Score: 18.606842425858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models. We find existing fine-tuning datasets frequently suffer from the "answer right but reasoning wrong" probelm, where correct final answers are derived from hallucinated, redundant, or logically invalid intermediate steps. This paper proposes EntroCoT, a unified framework for automatically identifying and refining low-quality CoT supervision traces. EntroCoT first proposes an entropy-based mechanism to segment the reasoning trace into multiple steps at uncertain junctures, and then introduces a Monte Carlo rollout-based mechanism to evaluate the marginal contribution of each step. By accurately filtering deceptive reasoning samples, EntroCoT constructs a high-quality dataset where every intermediate step in each reasoning trace facilitates the final answer. Extensive experiments on mathematical benchmarks demonstrate that fine-tuning on the subset constructed by EntroCoT consistently outperforms the baseslines of full-dataset supervision.

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