The October 6, 2025 paper introduces **Multi-Agent Tool-Integrated Policy Optimization (MATPO)**, a novel reinforcement learning framework designed to improve the performance of large language models (LLMs) in complex, knowledge-intensive tasks. MATPO addresses the limitations of single-agent systems, such as context length and noisy tool outputs, by adopting a **multi-agent architecture** that includes a **planner-agent** and specialized **worker-agents**. Crucially, this framework utilizes a **multi-agent-in-one-model** approach, allowing a single LLM instance to take on distinct roles through role-specific prompts, which enhances computational efficiency compared to using multiple separate LLMs. The paper details the **principled credit assignment mechanism** derived from the multi-agent policy gradient and provides experimental evidence demonstrating that MATPO **outperforms single-agent baselines** across several deep search benchmarks. The authors conclude with practical insights and future research directions for multi-agent reinforcement learning.Source:https://arxiv.org/pdf/2510.04678

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