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papersTODAY 04:00 UTC

Variance-Penalized Actor-Critic Method Avoids a Second Critic for Risk-Sensitive RL

Researchers propose a nonparametric approach to variance-penalized reinforcement learning that trades expected return for policy stability without training a separate variance critic. The work frames risk-sensitive RL through statistical inference, aiming to cut the extra computation and complexity that online variance estimation usually requires. It is a new arXiv preprint in machine learning.

reinforcement-learning/arxiv/risk-sensitive/research

papersSEP 10 04:00 UTC

BRACE paper proposes anchored Bellman-residual correction for stale critics in asynchronous RL

A new arXiv preprint introduces BRACE, a method aimed at value-function staleness in asynchronous reinforcement learning. As training of language models increasingly relies on asynchronous setups, delays between acting and learning bias the critic toward outdated policies, while prior asynchronous-training fixes targeted only the actor. The proposed approach applies an anchored Bellman-residual correction to keep the critic aligned with the current policy.

reinforcement-learning/llm-training/training