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RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization
arXiv – CS AI|Siwei Zhang, Yun Xiong, Xi Chen, Zi'an Jia, Renhong Huang, Jiarong Xu, Jiawei Zhang||1 views
🤖AI Summary
Researchers introduce RAPO (Retrieval-Augmented Policy Optimization), a new reinforcement learning framework that improves LLM agent training by incorporating retrieval mechanisms for broader exploration. The method achieves 5% performance gains across 14 datasets and 1.2x faster training efficiency by using hybrid-policy rollouts and retrieval-aware optimization.
Key Takeaways
- →RAPO addresses limitations of existing Agentic RL methods that rely solely on on-policy exploration paradigms.
- →The framework introduces two-phase training: Hybrid-policy Agentic Rollout and Retrieval-aware Policy Optimization.
- →The method enables LLM agents to reason over retrieved off-policy step-level traces for expanded exploration.
- →RAPO demonstrates 5% average performance improvement across fourteen datasets in three agentic reasoning tasks.
- →The approach delivers 1.2x faster training efficiency compared to existing methods.
#llm#reinforcement-learning#ai-agents#policy-optimization#retrieval-augmented#machine-learning#research#arxiv
Read Original →via arXiv – CS AI
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