Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Authors: name: Xufang Luo, name: Yuge Zhang, name: Zhiyuan He, name: Zilong Wang, name: Siyun Zhao, name: Dongsheng Li, name: Luna K. Qiu, name: Yuqing Yang

We present Agent Lightning, a flexible and extensible framework that enables
Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for
any AI agent. Unlike existing methods that tightly couple RL training with
agent or rely on sequence concatenation with masking, Agent Lightning achieves
complete decoupling between agent execution and training, allowing seamless
integration with existing agents developed via diverse ways (e.g., using
frameworks like LangChain, OpenAI Agents SDK, AutoGen, and building from
scratch) with almost ZERO code modifications. By formulating agent execution as
Markov decision process, we define an unified data interface and propose a
hierarchical RL algorithm, LightningRL, which contains a credit assignment
module, allowing us to decompose trajectories generated by ANY agents into
training transition. This enables RL to handle complex interaction logic, such
as multi-agent scenarios and dynamic workflows. For the system design, we
introduce a Training-Agent Disaggregation architecture, and brings agent
observability frameworks into agent runtime, providing a standardized agent
finetuning interface. Experiments across text-to-SQL, retrieval-augmented
generation, and math tool-use tasks demonstrate stable, continuous
improvements, showcasing the framework's potential for real-world agent
training and deployment.