Entropy-Guided Reasoning Compression
Hourun Zhu, Yang Gao, Wenlong Fei, Jiawei Li, Huashan Sun

TL;DR
This paper introduces an entropy-guided training framework to effectively compress reasoning chains in large models, reducing length to 20% while maintaining or improving accuracy across mathematical benchmarks.
Contribution
It identifies the entropy conflict in reasoning compression and proposes a novel entropy-guided approach to balance exploration and efficiency in reasoning chains.
Findings
Reasoning length reduced to 20% of original
Maintains or surpasses baseline accuracy
Effective across six mathematical benchmarks
Abstract
Large reasoning models have demonstrated remarkable performance on complex reasoning tasks, yet the excessive length of their chain-of-thought outputs remains a major practical bottleneck due to high computation cost and poor deployability. Existing compression methods have achieved partial success but overlook a crucial phenomenon in the training process -- the entropy conflict. During compression training, entropy decreases, leading to shorter reasoning but limited exploration, while accuracy-oriented objectives increase entropy, lengthening reasoning chains. This can cause the model to get stuck in a local dilemma. Our analysis further reveals the origin of the entropy conflict: many high-entropy tokens are logical connectors that receive larger gradients and are encouraged under the performance objective, while the compression objective simultaneously penalizes these potentially…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Reinforcement Learning in Robotics · AI-based Problem Solving and Planning
