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Hindsight-Anchored Policy Optimization

topic1 events
papersTODAY 04:00 UTC

Paper Proposes Hindsight-Anchored Policy Optimization for LLM Reasoning

A new arXiv paper introduces Hindsight-Anchored Policy Optimization, a method for training large language models with verifiable rewards. It uses hindsight learning combined with a Thompson sampling-inspired adaptive gate to address cold-start problems in sparse-reward, on-policy training. The approach builds on mixed-policy methods that blend off-policy and on-policy data.

reinforcement-learning/llm-reasoning/arxiv/training