critic-free reinforcement learning

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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

papersSEP 12 04:00 UTC

Belief-Shift Branching Targets Credit Assignment in Tree-Structured RL

A new arXiv paper proposes forking rollout trees at points where the model's beliefs shift, rather than at arbitrary intermediate steps, to assign credit in critic-free reinforcement learning with verifiable rewards. Because each fork adds sampling cost, the authors argue that concentrating branches on belief changes yields step-level value estimates more efficiently. The approach is aimed at improving how tree-structured rollouts trade compute for credit assignment.

reinforcement-learning/credit-assignment/research/efficiency