MiniMax M2 enhances real-world agent generalization with interleaved thinking and full-trajectory training

AI Impact Summary

MiniMax M2 emphasizes robustness to real-world perturbations by training on full-trajectory generalization and interleaved thinking, addressing a common gap where benchmark success doesn't translate to production reliability. The approach shifts from merely expanding tool counts to ensuring resilience across tool info, system prompts, user goals, and environments, implying production teams must preserve long-session context and manage richer data pipelines for training. Adoption will likely yield fewer failures when toolsets or prompts change, but requires investment in data collection and tooling to capture and replay complete task trajectories.