Yi-Bo Wang

Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question about the sampling distribution itself: under a fixed rollout budget, which distribution over states minimizes the variance of the policy-gradient estimator? The answer...