Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
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.
691d3c0c0247f3d258ef3235
Mingyue Cheng
691d3c0c0247f3d258ef3236
Jie Ouyang
691d3c0c0247f3d258ef3237
Shuo Yu
691d3c0c0247f3d258ef3238
Ruiran Yan
691d3c0c0247f3d258ef3239
Yucong Luo
691d3c0c0247f3d258ef323a
Zirui Liu
691d3c0c0247f3d258ef323b
Daoyu Wang
claimed_verified
2025-11-21T09:02:14.763Z
660176db573abbcdb9d76d61
https://cdn-avatars.huggingface.co/v1/production/uploads/660176db573abbcdb9d76d61/okKC96omVuRopecYlKs93.jpeg
Daoyu Wang
Melmaphother
user
Melmaphother
691d3c0c0247f3d258ef323c
Qi Liu
691d3c0c0247f3d258ef323d
Enhong Chen
Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
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.
A new training framework for RL-based LLM Agents is introduced, extending MDP methodology and demonstrating effectiveness on Multihop QA tasks.
Reinforcement Learning LLM Agents Markov Decision Process modular framework flexible framework user-friendly framework RL approaches Multihop QA