Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

This paper introduces **Chain-of-Agents (CoA)**, a novel method for **Large Language Models (LLMs)** to solve complex problems by simulating **multi-agent collaboration** within a single model. Unlike traditional **Tool-Integrated Reasoning (TIR)** methods, CoA allows for flexible integration of various **role-playing agents and tools** in an end-to-end fashion. The research details a **multi-agent distillation framework** and **agentic reinforcement learning (RL)** to train these **Agent Foundation Models (AFMs)**. Empirical studies showcase AFM's **superior performance and efficiency** across diverse benchmarks, including web navigation, code generation, and mathematical reasoning, ultimately making the entire project **open-source** to foster further development in agent models.