Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
This work addresses the problem of training LLM agents for complex tasks like tool use, but it appears incremental as it builds on existing RL methodologies without claiming major breakthroughs.
The paper tackles the challenge of applying reinforcement learning to train large language model agents by introducing Agent-R1, a modular framework, and demonstrates its effectiveness on multihop QA benchmarks with initial validation.
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.