5 focuses on the 'industrialization' of agent training through asynchronous reinforcement learning. It solves the efficiency problem where GPUs sit idle during long agent actions by separating the 'experience generation' from the 'model training' phase.
Training agents for industrial-scale deployment requires extreme stability and data throughput. We present Minimax M2.5, a model trained using a novel asynchronous RL architecture designed to process massive volumes of agentic trajectories. We address three primary challenges: handling long feedback loops, maintaining stability during large-scale RL, and ensuring diversity across tool-use tasks. Our 'dual-factory' generation and training pipeline ensures that GPUs are never idle, resulting in a model that excels at complex toolchains and real-world decision-making with 60% lower training latency.
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