New analysis shows covariance-based entropy control outperforms traditional regularization in reinforcement learning for language models

arXiv cs.LG · April 14, 2026

AI Summary

•Researchers developed a unified theoretical framework analyzing entropy dynamics in RL-enhanced large language models under softmax parameterization
•Policy entropy collapse during training causes premature convergence and performance saturation, limiting scalable RL applications
•Traditional entropy regularization introduces persistent bias that leads to suboptimal policies, while covariance-based methods selectively regularize sparse high-covariance tokens
•Covariance-based approaches achieve asymptotic unbiasedness, offering a more efficient alternative to dense entropy regularization

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