Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent
  Distillation and Agentic RL
Authors: name: Weizhen Li, name: Jianbo Lin, name: Zhuosong Jiang, name: Jingyi Cao, name: Xinpeng Liu, name: Jiayu Zhang, name: Zhenqiang Huang, name: Qianben Chen, name: Weichen Sun, name: Qiexiang Wang, name: Hongxuan Lu, name: Tianrui Qin, name: Chenghao Zhu, name: Yi Yao, name: Shuying Fan, name: Xiaowan Li, name: Tiannan Wang, name: Pai Liu, name: King Zhu, name: He Zhu, name: Dingfeng Shi, name: Piaohong Wang, name: Yeyi Guan, name: Xiangru Tang, name: Minghao Liu, name: Yuchen Eleanor Jiang, name: Jian Yang, name: Jiaheng Liu, name: Ge Zhang, name: Wangchunshu Zhou

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.