FlexMARL: Graph-Driven Rollout-Training Co-Design for LLM-Based Multi-Agent Reinforcement Learning

Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) offer a promising approach to collaborative tasks, but applying reinforcement learning to these systems remains challenging because classical multi-agent reinforcement learning assumptions do not fully capture their dependency structures, heterogeneous roles, and autoregressive outputs. This article provides a tutorial-style overview of the transition from reinforcement learning to reinforcement fine-tuning in single-agent and multi-agent settings, highlighting the differences between classical MARL and LLM-based multi-agent optimization. We introduce Multi-Agent Reinforcement Fine-Tuning (MARFT) and formalize LaMAS optimization as a Flexible Markov Game (Flex-MG). The MARFT framework combines predecessor-conditioned policy optimization, a centralized critic, parameter-efficient adapters, and token-level credit assignment. Our token-level analysis clarifies how within-action discounting affects returns and establishes consistency with action-level returns when the token discount is one. Experiments on fixed two- to four-agent mathematical and coding workflows compare MARFT with and without policy parameter sharing against IPPO. Both variants report higher mean evaluation scores in all 24 benchmark-team-size settings, while the variant without parameter sharing shows the clearest advantage in final training reward. Role-specification ablations examine how prescribed roles affect training. We conclude with application perspectives and open challenges, including dynamic workflows, sample efficiency, and system-level reliability. Code: https://github.com/jwliao-ai/MARFT.

Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly in multi-domain operations (MDO). A central challenge in cooperative MARL is determining how agents should coordinate: existing approaches must either hand-specify graph topology, rely on proximity-based heuristics, or learn structure entirely from environment interaction; all of which are brittle, semantically uninformed, or data-intensive. We investigate whether large language models (LLMs) can generate useful coordination graph priors for MARL by using minimal natural language descriptions of agent observations to infer latent coordination patterns. These priors are integrated into MARL algorithms via graph convolutional layers within a graph neural network (GNN)-based pipeline, and evaluated on four cooperative scenarios from the Multi-Agent Particle Environment (MPE) benchmark against baselines spanning the full spectrum of coordination modeling, from independent learners to state-of-the-art graph-based methods. We further ablate across five compact open-source LLMs to assess the sensitivity of prior quality to model choice. Our results provide the first quantitative evidence that LLM-derived graph priors can enhance coordination and adaptability in dynamic multi-agent environments, and demonstrate that models as small as 1.5B parameters are sufficient for effective prior generation.

Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally, RL environments run on the CPU, which limits their scalability with typical academic compute. However, recent advancements in JAX have enabled the wider use of hardware acceleration, enabling massively parallel RL training pipelines and environments. While this has been successfully applied to single-agent RL, it has not yet been widely adopted for multi-agent scenarios. In this paper, we present JaxMARL, the first open-source, Python-based library that combines GPU-enabled efficiency with support for a large number of commonly used MARL environments and popular baseline algorithms. Our experiments show that, in terms of wall clock time, our JAX-based training pipeline is around 14 times faster than existing approaches, and up to 12500x when multiple training runs are vectorized. This enables efficient and thorough evaluations, potentially alleviating the evaluation crisis in the field. We also introduce and benchmark SMAX, a JAX-based approximate reimplementation of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL. The code is available at https://github.com/flairox/jaxmarl.