Aligning to What? Rethinking Agent Generalization in MiniMax M2 (huggingface.co)

The AI community has welcomed the introduction of MiniMax M2, an advanced agent model capable of tackling complex tasks. The development team emphasizes the need for alignment not only with benchmark tests, which assess foundational skills, but also with real-world user applications. This dual alignment approach addresses a critical issue in the AI field: ensuring agents perform reliably across varied contexts and toolsets. A key discovery during M2's development is the necessity of "Interleaved Thinking," where an agent's reasoning occurs continuously throughout a task, rather than at the start. This methodology enhances the agent's ability to manage long tasks and adapt to unpredictable changes in its environment. Through rigorous testing and a refined understanding of agent generalization—beyond just tool adaptation to include all aspects of the operational ecosystem—MiniMax M2 has shown encouraging results in terms of instruction-following and tool usage capabilities. As the team prepares to delve deeper into unexplored areas, they invite researchers to test M2 and contribute to the ongoing pursuit of advancing artificial general intelligence (AGI).