Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning

Ali Nemati7 hours ago23 sec read25 views

Researchers introduced Curvature-Aware Policy Optimization (CAPO) to enhance policy gradient methods in reinforcement learning, addressing stability issues that require excessive training samples and computational resources. By identifying and mitigating unstable updates through curvature information, CAPO achieves significant improvements in sample efficiency for large language model reasoning tasks without substantial hyperparameter tuning.

Read the full article at arXiv cs.LG (ML)

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