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

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