P^2O method enhances LLM reasoning by optimizing prompts and policies

Researchers have developed a new method called P^2O (Joint Policy and Prompt Optimization) to address the issue of advantage collapse in Reinforcement Learning with Verifiable Rewards (RLVR) for large language models. This technique alternates between continuous policy updates and discrete prompt evolution, using the GEPA algorithm to discover effective prompts for challenging samples. By distilling these prompts into the model's parameters, P^2O improves out-of-distribution generalization and achieves up to a 9.5% performance increase over existing methods. AI

影响 Introduces a novel approach to enhance LLM reasoning by combining prompt engineering with reinforcement learning, potentially improving performance on complex tasks.

排序理由 This is a research paper detailing a new method for improving LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Kaiqi Zhang
• LLM
• RLVR

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →