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A paper proposing learned control variates built from ML classifiers to reduce variance in rare grid-outage probability estimation for EV charging loads, showing boosted trees can remove ~60% of variance and improving blackout-day planning for Surabaya's distribution network.
The paper introduces Advantage-Based Control Variates (ABC), an advantage–value formulation of trajectory-level control variates for RL with verifiable rewards, combined with Direct Advantage Estimation into a single actor-critic algorithm that matches GRPO's performance on mathematical reasoning with far fewer online optimization steps.
This paper proposes shrinkage baselines for reinforcement learning with verifiable rewards to reduce variance in policy gradient estimators and improve training stability.
This paper studies fixed-confidence best-arm identification where costly reward observations are paired with cheap but correlated proxy scores from generative models. It proposes the PROBE algorithm that uses control-variate adjustment and upper certificates on residual variance to achieve oracle-like sample complexity.