Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
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| Format: | Preprint |
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2025
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| _version_ | 1866912541675880448 |
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| author | Li, Weizhen Lin, Jianbo Jiang, Zhuosong Cao, Jingyi Liu, Xinpeng Zhang, Jiayu Huang, Zhenqiang Chen, Qianben Sun, Weichen Wang, Qiexiang Lu, Hongxuan Qin, Tianrui Zhu, Chenghao Yao, Yi Fan, Shuying Li, Xiaowan Wang, Tiannan Liu, Pai Zhu, King Zhu, He Shi, Dingfeng Wang, Piaohong Guan, Yeyi Tang, Xiangru Liu, Minghao Jiang, Yuchen Eleanor Yang, Jian Liu, Jiaheng Zhang, Ge Zhou, Wangchunshu |
| author_facet | Li, Weizhen Lin, Jianbo Jiang, Zhuosong Cao, Jingyi Liu, Xinpeng Zhang, Jiayu Huang, Zhenqiang Chen, Qianben Sun, Weichen Wang, Qiexiang Lu, Hongxuan Qin, Tianrui Zhu, Chenghao Yao, Yi Fan, Shuying Li, Xiaowan Wang, Tiannan Liu, Pai Zhu, King Zhu, He Shi, Dingfeng Wang, Piaohong Guan, Yeyi Tang, Xiangru Liu, Minghao Jiang, Yuchen Eleanor Yang, Jian Liu, Jiaheng Zhang, Ge Zhou, Wangchunshu |
| contents | Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Li, Weizhen Lin, Jianbo Jiang, Zhuosong Cao, Jingyi Liu, Xinpeng Zhang, Jiayu Huang, Zhenqiang Chen, Qianben Sun, Weichen Wang, Qiexiang Lu, Hongxuan Qin, Tianrui Zhu, Chenghao Yao, Yi Fan, Shuying Li, Xiaowan Wang, Tiannan Liu, Pai Zhu, King Zhu, He Shi, Dingfeng Wang, Piaohong Guan, Yeyi Tang, Xiangru Liu, Minghao Jiang, Yuchen Eleanor Yang, Jian Liu, Jiaheng Zhang, Ge Zhou, Wangchunshu Artificial Intelligence Computation and Language Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL. |
| title | Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2508.13167 |