Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning

2025-11-18

AI Summary

Existing reinforcement learning (RL) training methods for large language model (LLM)-based agents lack principled methodologies and scalable frameworks tailored to their unique architectural and behavioral characteristics. Method: This paper redefines the RL methodology for LLM agents by systematically extending the Markov Decision Process (MDP) formalism—explicitly modeling agent state, action space (e.g., tool invocation, multi-step reasoning steps), and reward design—and building Agent-R1, a modular, extensible RL training framework supporting rich environment interactions. It employs end-to-end RL to jointly optimize policy, reasoning, and tool-use capabilities. Contribution/Results: Evaluated on multi-hop question answering benchmarks, Agent-R1 achieves significant performance gains on complex reasoning tasks, demonstrating both empirical effectiveness and strong generalization across diverse agent behaviors and environments. The framework provides a foundation for scalable, interpretable, and task-agnostic LLM agent training.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Developing reinforcement learning methods for LLM agents
• Creating flexible training frameworks for agent-environment interaction
• Addressing challenges in applying RL to complex problem-solving tasks

Innovation

• End-to-end reinforcement learning for LLM agents
• Modular framework for diverse task adaptation
• Extends MDP to define LLM agent components