Aligning to What? Rethinking Agent Generalization in MiniMax M2

It's been fantastic to see the community dive into our new MiniMax M2, with many highlighting its impressive skills in complex agentic tasks. This is particularly exciting for me, as my work was centered on the agent alignment part of its post-training. In this post, I'd like to share some of the key insights and lessons we learned during that process.

The Real Agent Alignment Problem: Benchmarks or Reality?

If you've worked with LLM Agents, you've felt this pain: the same model can feel brilliant in one framework and useless in another. An agent might crush a tool-use leaderboard but fail spectacularly at a simple, real-world task. This gap between benchmark performance and practical usability is one of the biggest challenges in the field.

When we designed M2, we knew we had to tackle this problem head-on. This led us to two core, and sometimes conflicting, objectives:

1. Excel on Open-Source Benchmarks. Benchmarks are essential for measuring "pure" capabilities. A benchmark like BrowseComp, for instance, tests for sophisticated search skills. While users will rarely ask a question as contrived as, "Find the paper where the third letter of the nth author's name is 'x'," a model that can solve it proves it has strong foundational abilities.
2. Generalize Robustly to the Real World. This is the harder, more important part. A great agent must perform reliably across unfamiliar tools, IDEs/CLIs, agent scaffolding, and user setups. It can't be a one-trick pony; it needs to generalize.

So, who do we align with? The answer is both. We align with benchmarks to build skill, but we must ultimately align with the user by ensuring those skills work everywhere.

While the methods for acing benchmarks are a deep topic for another day, I want to focus on that second, trickier objective: How do we train an agent for the wild?

The Need for Interleaved Thinking

This principle became a cornerstone of M2's effectiveness.