Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent
  Distillation and Agentic RL
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.
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Weizhen Li
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Jianbo Lin
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Zhuosong Jiang
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Jingyi Cao
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Xinpeng Liu
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Jiayu Zhang
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Zhenqiang Huang
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Qianben Chen
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Weichen Sun
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Qiexiang Wang
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Hongxuan Lu
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Tianrui Qin
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Chenghao Zhu
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Yi Yao
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Shuying Fan
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Xiaowan Li
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Tiannan Wang
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Pai Liu
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King Zhu
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Piaohong Wang
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Yeyi Guan
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Xiangru Tang
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Minghao Liu
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Ge Zhang
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Wangchunshu Zhou
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent
  Distillation and Agentic RL
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.
Chain-of-Agents (CoA) paradigm enables end-to-end complex problem-solving in LLMs through dynamic agent activation, improving performance via multi-agent distillation and agentic reinforcement learning.
large language models multi-agent systems deep research vibe coding mathematical reasoning prompt/workflow engineering agent frameworks chain-of-agents tool agents role-playing agents multi-agent distillation agentic supervised fine-tuning agentic reinforcement learning Agent Foundation Models AFMs web agent code agent verifiable agentic tasks