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.

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