It has been implausible to see the community dive into our recent MiniMax M2, with many highlighting its impressive skills in complex agentic tasks. This is especially exciting for me, as my work was centered on the agent alignment a part of its post-training. On this post, I’d wish to share a number of the key insights and lessons we learned during that process.
The Real Agent Alignment Problem: Benchmarks or Reality?
In case you’ve worked with LLM Agents, you have felt this pain: the identical model can feel sensible in a single framework and useless in one other. An agent might crush a tool-use leaderboard but fail spectacularly at an easy, real-world task. This gap between benchmark performance and practical usability is one in every of the largest challenges in the sphere.
Once we designed M2, we knew we needed to tackle this problem head-on. This led us to 2 core, and sometimes conflicting, objectives:
- Excel on Open-Source Benchmarks. Benchmarks are essential for measuring “pure” capabilities. A benchmark like BrowseComp, for example, tests for classy search skills. While users will rarely ask an issue as contrived as, “Find the paper where the third letter of the nth writer’s name is ‘x’,” a model that may solve it proves it has strong foundational abilities.
- Generalize Robustly to the Real World. That is the harder, more essential part. An awesome agent must perform reliably across unfamiliar tools, IDEs/CLIs, agent scaffolding, and user setups. It may possibly’t be a one-trick pony; it must generalize.
So, who will we align with? The reply is each. We align with benchmarks to construct skill, but we must ultimately align with the user by ensuring those skills work all over the place.
While the methods for acing benchmarks are a deep topic for an additional day, I would like to give attention to that second, trickier objective: How will we train an agent for the wild?
The Need for Interleaved Pondering
Early within the project, we hit a frustrating wall. Agent performance was inconsistent, and we struggled to diagnose why. After many discussions, especially with Professor @Junxian He and @Wenhu Chen, we arrived at our first major conclusion: Agents require Interleaved Pondering.
Which means that an agent’s internal monologue—its “considering”—can and may occur at any point during a task, not only once at the start like a regular reasoning model. This design is critical for 2 reasons:
- Maintaining Give attention to Long-Horizon Tasks. Complex agent tasks have extremely long contexts. A single thought process at first is not enough to keep up instruction-following and coherence.
- Adapting to External Perturbations. That is the crucial difference. Agent tasks introduce constant, unpredictable perturbations from the skin world (i.e., tool outputs). The model have to be robust enough to handle these perturbations, diagnose errors, and extract useful information. The “considering” process allows the model to always re-evaluate and adapt to recent information from the environment.
This principle became a cornerstone of M2’s effectiveness.
Pro Tip for M2 Users: Because M2 relies on Interleaved Pondering, its context is its memory. For best performance, you should retain the complete session history, including the considering steps. We have noticed that much of the community feedback about performance gaps stems from by chance discarding this vital context, which is a typical practice with simpler reasoning models.
True Generalization is About Perturbation
Our initial theory was easy: tool scaling is agent generalization.
We began with a minimal set of tools (a Python interpreter, search engine, a browser) to construct a baseline of tool-calling capability. The roadmap was clear: scale up the number and number of tools, and the agent’s ability to generalize to unseen tools would naturally follow.
At first, this worked. Our benchmark scores climbed to respectable levels. But as we dug deeper, we realized we were solving the unsuitable problem. The model aced the tests, but when we modified the environment even barely—like swapping to a unique scaffolding framework—its performance would plummet. We were still removed from our goal of a “practically useful” model.
This led to our second, more profound realization: Agent generalization is just not nearly adapting to recent tools; it’s about adapting to perturbations across the model’s entire operational space.
This sounds abstract, so let’s break it down. Take into consideration every little thing that may change in a single agent task:
- The Tool Info and available toolset.
- The System Prompt defining the agent’s persona and rules.
- The User Prompt and its specific goal.
- The Environment itself (files, codebases, APIs).
- The Tool Responses returned at each step.
Our old “tool scaling” approach only addressed the primary item. It ignored perturbations in all the opposite parts of the method.
Armed with this recent understanding, our team built a comprehensive data pipeline designed for full-trajectory generalization. The information it generates trains the model to be stable against perturbations at every step. The outcomes have been incredibly encouraging. In internal tests, we threw obscure, “cold-start” scaffolding at M2—frameworks we might barely considered—and its performance exceeded our expectations. Each its tool-calling and instruction-following abilities generalized beautifully.
What’s Next?
Our work on M2 taught us an immense amount about agents, generalization, and data, nevertheless it has opened up more questions than it answered. A lot of our ideas are still on the whiteboard. In the approaching months, we will probably be exploring these frontiers much more deeply, and we won’t wait to bring you the subsequent generation of powerful and genuinely useful models.
Getting Involved
- Use the Model: We sincerely hope you may put M2 to the test. You may access it through our official channels or find the open-sourced version to conduct your personal research.
- Join Our Team: If these are the sorts of challenges that excite you, we’re hiring. We’re all the time on the lookout for passionate people to affix us within the mission to construct AGI. Please send us your resume!
