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.
@article{arxiv.2601.03769,
title = {EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation},
author = {Zihang Li and Yuhang Wang and Yikun Zong and Wenhan Yu and Xiaokun Yuan and Runhan Jiang and Zirui Liu and Tong Yang and Arthur Jiang},
journal= {arXiv preprint arXiv:2601.03769},
year = {2026}
}