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