Covariance-based entropy control selectively regularizes high-covariance tokens in softmax policies and achieves asymptotic unbiasedness upon annealing, unlike traditional regularization which introduces dense bias and alters the stationary distribution.
Optimization methods for large- scale machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
A Comparative Theoretical Analysis of Entropy Control Methods in Reinforcement Learning
Covariance-based entropy control selectively regularizes high-covariance tokens in softmax policies and achieves asymptotic unbiasedness upon annealing, unlike traditional regularization which introduces dense bias and alters the stationary distribution.