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Policy Mirror Descent

topic1 events
papersTODAY 04:00 UTC

Bellman Policy Optimization: Critic-Free RL Method for LLM Reasoning

Researchers present Bellman Policy Optimization (BPO), a reinforcement learning approach for training large language models with verifiable rewards that does not require a separate critic network. The method is derived from Policy Mirror Descent and targets autoregressive generation. It aims to improve reasoning performance in LLMs while simplifying the training setup.

reinforcement-learning/llm-reasoning/research/training