Multi-Agent Reinforcement Learning for Autonomous Large Language Model Collaboration

Warjas Yogi; Urbano Isern; Geronimo Mocenigo; Milton Jansson

Authors

Warjas Yogi Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA. Author
Urbano Isern Department of Computer Science, University of New Hampshire, Durham, NH, USA. Author
Geronimo Mocenigo Department of Computer Science, University of North Texas, Denton, TX, USA. Author
Milton Jansson Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL, USA. Author

Keywords:

multi-agent reinforcement learning; large language models; autonomous collaboration; socio-technical systems; governance; robustness; fairness; sustainability; deployment; infrastructure

Abstract

Large language models have moved rapidly from isolated text generators to components within broader agentic systems, yet the most consequential next step is not simply making individual models more capable. It is enabling many language-model agents to collaborate autonomously under conditions that remain robust, accountable, fair, and sustainable. This paper examines multi-agent reinforcement learning as a system-level framework for autonomous large language model collaboration. It argues that collaboration among language-model agents should be understood as a socio-technical infrastructure problem involving architecture, credit assignment, communication protocols, deployment economics, governance, and policy. The analysis first reviews the shift from single-agent reinforcement learning and language-model assistance to multi-agent language ecologies. It then examines centralized, decentralized, hierarchical, and market-oriented coordination structures, along with their trade-offs in latency, resilience, interpretability, and control. The paper discusses learning dynamics, including non-stationarity, credit assignment, preference alignment, and verbal reinforcement, and considers how these dynamics shape emergent norms and failure modes. It further analyzes infrastructure and sustainability pressures, especially compute intensity, orchestration overhead, observability, and long-term maintenance. Robustness, security, fairness, and governance are treated as first-order design constraints rather than post hoc corrections. Through case illustrations in software development, scientific assistance, public administration, and interactive simulation, the paper shows that autonomous collaboration can improve coverage and adaptability while introducing new accountability gaps. The conclusion recommends layered governance, auditable coordination traces, resource-aware scheduling, and evaluation regimes that measure collective behavior rather than only individual model performance.

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Multi-Agent Reinforcement Learning for Autonomous Large Language Model Collaboration

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