Aligning to What? Rethinking Agent Generalization in MiniMax M2

The paper presents MiniMax M2, an advanced reinforcement learning agent designed to improve generalization in multi-agent environments. Key innovations include a novel architecture that combines hierarchical reinforcement learning with a minimax strategy, enabling the agent to effectively handle complex decision-making scenarios. This research is significant for practitioners as it addresses the challenges of agent adaptability and robustness in dynamic settings, potentially enhancing the performance of AI systems in competitive environments.