arXiv:2504.16129v4 Announce Type: replace-cross 
Abstract: LLM-based Multi-Agent Systems have demonstrated remarkable capabilities in addressing complex, agentic tasks, from generating high-quality presentation slides to even conducting sophisticated scientific research. Meanwhile, RL has been widely recognized for its effectiveness in enhancing agent intelligence, but limited research has investigated the fine-tuning of LaMAS using foundational RL techniques. Moreover, the direct application of MARL methods to LaMAS introduces significant challenges, stemming from the unique characteristics and mechanisms inherent to LaMAS. To address these challenges, this article presents a comprehensive study of LLM-based MARL and proposes a novel paradigm termed Multi-Agent Reinforcement Fine-Tuning (MARFT). We introduce a brand-new MG called Flex-MG, which aligns with the LaMAS optimization in real-world applications and a universal algorithmic framework tailored specifically for LaMAS, outlining the conceptual foundations, key distinctions, and practical implementation strategies. We review the evolution from RL to RFT, setting the stage for a parallel analysis in the multi-agent domain. In the context of LaMAS, we elucidate critical differences between MARL and MARFT. These differences motivate a transition toward a LaMAS-oriented formulation of RFT. Central to this work is a robust and scalable MARFT framework. We detail the core algorithm and provide a complete, open-source implementation to facilitate adoption and further research. The latter sections of the paper explore real-world application perspectives and opening challenges in MARFT. By bridging theoretical underpinnings with practical methodologies, this work serves as a roadmap for researchers seeking to advance MARFT toward resilient and adaptive solutions in agentic systems. Our implementation of the proposed framework is publicly available at: https://github.com/jwliao-ai/MARFT.

تسلط الورقة البحثية الأخيرة حول تحسين التعزيز متعدد الوكلاء الضوء على القدرات المذهلة للأنظمة متعددة الوكلاء المعتمدة على LLM في معالجة المهام المعقدة، مثل إنشاء عروض تقديمية عالية الجودة وإجراء أبحاث علمية متقدمة. هذه الأبحاث مهمة لأنها تستكشف تحسين هذه الأنظمة باستخدام تقنيات التعلم المعزز الأساسية، مما قد يؤدي إلى تعزيز ذكاء الوكلاء وتوسيع التطبيقات في مجالات متنوعة.

El reciente artículo sobre el Fine-Tuning de Refuerzo Multi-Agente destaca las impresionantes capacidades de los Sistemas Multi-Agente basados en LLM para abordar tareas complejas, como crear presentaciones de alta calidad y realizar investigaciones científicas avanzadas. Esta investigación es significativa ya que explora el ajuste fino de estos sistemas utilizando técnicas fundamentales de aprendizaje por refuerzo, lo que podría llevar a una inteligencia de agentes mejorada y a aplicaciones más amplias en diversos campos.

Le récent article sur le Fine-Tuning par Renforcement Multi-Agent met en lumière les capacités impressionnantes des systèmes multi-agents basés sur LLM pour s'attaquer à des tâches complexes, telles que la création de présentations de haute qualité et la réalisation de recherches scientifiques avancées. Cette recherche est importante car elle explore le fine-tuning de ces systèmes en utilisant des techniques fondamentales d'apprentissage par renforcement, ce qui pourrait conduire à une intelligence des agents améliorée et à des applications plus larges dans divers domaines.

The recent paper on Multi-Agent Reinforcement Fine-Tuning highlights the impressive capabilities of LLM-based Multi-Agent Systems in tackling complex tasks, such as creating high-quality presentations and conducting advanced scientific research. This research is significant as it explores the fine-tuning of these systems using foundational reinforcement learning techniques, which could lead to enhanced agent intelligence and broader applications in various fields.

MARFT: Multi-Agent Reinforcement Fine-Tuning

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MARFT: Multi-Agent Reinforcement Fine-Tuning

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