MiniMax M2 Reveals Agent Generalization Strategy

The MiniMax M2 development team revealed key insights into agent alignment aimed at closing the gap between benchmark scores and real-world usability.

The main technical approaches are as follows:

• Interleaved Thinking: Rather than having the agent reason only at the start of a task, this design allows it to perform internal thinking at any point during the task process. This plays a key role in maintaining long-horizon task context and responding in real time to perturbations from the external environment, such as tool outputs, in order to diagnose errors and adapt.
• Perturbation-based Generalization: The team emphasized that true agent generalization goes beyond simple tool expansion, and means operating stably even under various perturbation situations such as changes in the environment or framework.

To achieve optimal performance when using the M2 model, it is essential to maintain the entire session history, including thinking steps.