# Agent Lightning: Train ANY AI Agents with Reinforcement Learning

**Authors:** Xufang Luo, Yuge Zhang, Zhiyuan He, Zilong Wang, Siyun Zhao, Dongsheng Li, Luna K. Qiu, Yuqing Yang  
**arXiv:** [2508.03680](https://arxiv.org/abs/2508.03680)  
**Date:** 2025-08-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2508.03680v1/

## Key points

- Authors introduce Agent Lightning as a versatile framework for training Large Language Models (LLMs) using Reinforcement Learning (RL)
- Agent Lightning allows seamless integration with existing agents developed through various frameworks like LangChain, OpenAI Agents SDK, AutoGen, or built from scratch with minimal code modifications
- Agent Lightning achieves a separation between agent execution and training by formulating agent execution as a Markov decision process
- The hierarchical RL algorithm called LightningRL includes a credit assignment module that enables the decomposition of trajectories generated by any agents into training transitions
- Experimental results across tasks like text-to-SQL conversion, retrieval-augmented generation, and math tool-use demonstrate stable and continuous improvements in performance

## Summary

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In their paper titled "Agent Lightning: Train ANY AI Agents with Reinforcement Learning," authors Xufang Luo, Yuge Zhang, Zhiyuan He, Zilong Wang, Siyun Zhao, Dongsheng Li, Luna K. Qiu, and Yuqing Yang introduce Agent Lightning as a versatile framework for training Large Language Models (LLMs) using Reinforcement Learning (RL). This framework allows for seamless integration with existing agents developed through various frameworks like LangChain, OpenAI Agents SDK, AutoGen, or built from scratch with minimal code modifications. between agent execution and training is achieved in Agent Lightning unlike existing methods that tightly couple RL training with the agent or rely on sequence concatenation with masking. By formulating agent execution as a Markov decision process, the authors define a unified data interface and propose a hierarchical RL algorithm called LightningRL. This algorithm includes a credit assignment module that enables the decomposition of trajectories generated by any agents into training transitions. This capability allows RL to handle complex interaction logic such as multi-agent scenarios and dynamic workflows effectively. The system design of Agent Lightning introduces a  and incorporates agent observability frameworks into agent runtime. This integration provides a standardized interface for fine-tuning agents. Experimental results across tasks like text-to-SQL conversion, retrieval-augmented generation, and math tool-use demonstrate stable and continuous improvements in performance. These findings highlight the framework's potential for real-world agent training and deployment in diverse applications within the AI domain. Overall, Agent Lightning offers a powerful solution for training any AI agents using Reinforcement Learning techniques while maintaining flexibility and compatibility with existing agent frameworks.