Researchers introduced Empirical Bayes Policy Optimization (EBPO) to stabilize Group Relative Policy Optimization (GRPO), addressing its instability issues in reinforcement learning scenarios with limited data and zero-reward environments. EBPO improves estimator accuracy and training stability by leveraging global policy statistics, outperforming GRPO across various benchmarks and demonstrating particular benefits for small group sizes and curriculum learning strategies.
Read the full article at arXiv cs.CL (NLP)
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Ali NematiWritten by Ali
