Chain-of-Agents: Multi-Agent Distillation for LLM Problem-Solving - Paper Overview

This source 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 multi-agent systems that require extensive manual engineering. The approach involves multi-agent distillation to create training data and agentic reinforcement learning (RL) to refine the model's capabilities, resulting in Agent Foundation Models (AFMs). AFMs demonstrate state-of-the-art performance across diverse benchmarks, including web navigation, code generation, and mathematical reasoning,…