AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911148006178816 |
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| author | Xi, Zhiheng Huang, Jixuan Liao, Chenyang Huang, Baodai Guo, Honglin Liu, Jiaqi Zheng, Rui Ye, Junjie Zhang, Jiazheng Chen, Wenxiang He, Wei Ding, Yiwen Li, Guanyu Chen, Zehui Du, Zhengyin Yao, Xuesong Xu, Yufei Chen, Jiecao Gui, Tao Wu, Zuxuan Zhang, Qi Huang, Xuanjing Jiang, Yu-Gang |
| author_facet | Xi, Zhiheng Huang, Jixuan Liao, Chenyang Huang, Baodai Guo, Honglin Liu, Jiaqi Zheng, Rui Ye, Junjie Zhang, Jiazheng Chen, Wenxiang He, Wei Ding, Yiwen Li, Guanyu Chen, Zehui Du, Zhengyin Yao, Xuesong Xu, Yufei Chen, Jiecao Gui, Tao Wu, Zuxuan Zhang, Qi Huang, Xuanjing Jiang, Yu-Gang |
| contents | Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive development, agents are expected to acquire knowledge and skills through exploration and interaction with the environment. Despite advances, the community still lacks a unified, interactive reinforcement learning (RL) framework that can effectively train such agents from scratch -- without relying on supervised fine-tuning (SFT) -- across diverse and realistic environments. To bridge this gap, we introduce AgentGym-RL, a new framework to train LLM agents for multi-turn interactive decision-making through RL. The framework features a modular and decoupled architecture, ensuring high flexibility and extensibility. It encompasses a wide variety of real-world scenarios, and supports mainstream RL algorithms. Furthermore, we propose ScalingInter-RL, a training approach designed for exploration-exploitation balance and stable RL optimization. In early stages, it emphasizes exploitation by restricting the number of interactions, and gradually shifts towards exploration with larger horizons to encourage diverse problem-solving strategies. In this way, the agent develops more diverse behaviors and is less prone to collapse under long horizons. We perform extensive experiments to validate the stability and effectiveness of both the AgentGym-RL framework and the ScalingInter-RL approach. Our agents match or surpass commercial models on 27 tasks across diverse environments. We offer key insights and will open-source the complete AgentGym-RL framework -- including code and datasets -- to empower the research community in developing the next generation of intelligent agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning Xi, Zhiheng Huang, Jixuan Liao, Chenyang Huang, Baodai Guo, Honglin Liu, Jiaqi Zheng, Rui Ye, Junjie Zhang, Jiazheng Chen, Wenxiang He, Wei Ding, Yiwen Li, Guanyu Chen, Zehui Du, Zhengyin Yao, Xuesong Xu, Yufei Chen, Jiecao Gui, Tao Wu, Zuxuan Zhang, Qi Huang, Xuanjing Jiang, Yu-Gang Machine Learning Artificial Intelligence Computation and Language Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive development, agents are expected to acquire knowledge and skills through exploration and interaction with the environment. Despite advances, the community still lacks a unified, interactive reinforcement learning (RL) framework that can effectively train such agents from scratch -- without relying on supervised fine-tuning (SFT) -- across diverse and realistic environments. To bridge this gap, we introduce AgentGym-RL, a new framework to train LLM agents for multi-turn interactive decision-making through RL. The framework features a modular and decoupled architecture, ensuring high flexibility and extensibility. It encompasses a wide variety of real-world scenarios, and supports mainstream RL algorithms. Furthermore, we propose ScalingInter-RL, a training approach designed for exploration-exploitation balance and stable RL optimization. In early stages, it emphasizes exploitation by restricting the number of interactions, and gradually shifts towards exploration with larger horizons to encourage diverse problem-solving strategies. In this way, the agent develops more diverse behaviors and is less prone to collapse under long horizons. We perform extensive experiments to validate the stability and effectiveness of both the AgentGym-RL framework and the ScalingInter-RL approach. Our agents match or surpass commercial models on 27 tasks across diverse environments. We offer key insights and will open-source the complete AgentGym-RL framework -- including code and datasets -- to empower the research community in developing the next generation of intelligent agents. |
| title | AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.08755 |