Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
Mingyue Cheng, Jie Ouyang, Shuo Yu, Ruiran Yan, Yucong Luo, Zirui Liu, Daoyu Wang, Qi Liu, Enhong Chen
2025-11-19
Summary
This paper explores how to better train powerful AI systems called Large Language Models (LLMs) to actively interact with their surroundings and solve complicated problems, like using tools to get things done.
What's the problem?
Currently, using a technique called Reinforcement Learning to train these LLM-based AI 'agents' is difficult and not well understood. There aren't clear guidelines on *how* to apply Reinforcement Learning specifically to LLMs, and there's a lack of good software tools that make it easy to experiment with and build these agents. It's hard to adapt existing methods to the unique way LLMs work.
What's the solution?
The researchers first clarified how Reinforcement Learning should be applied to LLM agents by building upon a standard framework called the Markov Decision Process. Then, they created a new software framework called Agent-R1. This framework is designed to be flexible, easy to use, and adaptable to different tasks and environments. They tested it on question-answering tasks that require multiple steps to find the answer, showing that their approach works.
Why it matters?
This work is important because it provides a clearer path for developing more capable AI agents powered by LLMs. By offering a well-defined approach and a user-friendly framework, it lowers the barrier to entry for researchers and developers, potentially leading to faster progress in building AI systems that can solve real-world problems through active interaction.
Abstract
Large Language Models (LLMs) are increasingly being explored for building Agents capable of active environmental interaction (e.g., via tool use) to solve complex problems. Reinforcement Learning (RL) is considered a key technology with significant potential for training such Agents; however, the effective application of RL to LLM Agents is still in its nascent stages and faces considerable challenges. Currently, this emerging field lacks in-depth exploration into RL approaches specifically tailored for the LLM Agent context, alongside a scarcity of flexible and easily extensible training frameworks designed for this purpose. To help advance this area, this paper first revisits and clarifies Reinforcement Learning methodologies for LLM Agents by systematically extending the Markov Decision Process (MDP) framework to comprehensively define the key components of an LLM Agent. Secondly, we introduce Agent-R1, a modular, flexible, and user-friendly training framework for RL-based LLM Agents, designed for straightforward adaptation across diverse task scenarios and interactive environments. We conducted experiments on Multihop QA benchmark tasks, providing initial validation for the effectiveness of our proposed methods and framework.