Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models
The paper discusses a new approach to understanding the internal mechanisms of Large Reasoning Models (LRMs) through a concept called Entropy-Gradient Inversion. This method reveals a correlation between token entropy and reasoning performance, leading to the development of a new optimization technique. Experimental results demonstrate that this approach significantly enhances reasoning capabilities in various benchmarks.
- ▪The study introduces Entropy-Gradient Inversion as a key concept for analyzing LRM reasoning mechanisms.
- ▪Correlation-Regularized Group Policy Optimization (CorR-PO) is proposed to improve reinforcement learning for reasoning tasks.
- ▪Extensive experiments show that CorR-PO outperforms existing methods in reasoning performance.
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Computer Science > Artificial Intelligence arXiv:2605.17770 (cs) [Submitted on 18 May 2026] Title:Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models Authors:Junyao Yang, Chen Qian, Kun Wang, Linfeng Zhang, Quanshi Zhang, Yong Liu, Dongrui Liu View a PDF of the paper titled Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models, by Junyao Yang and 6 other authors View PDF HTML (experimental) Abstract:The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reasoning, unlocking state-of-the-art performance in complex mathematical and logical tasks.
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