Intelligence with Everyone: RL @ MiniMax, with Olive Song, from AIE NYC & Inference by Turing Post

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. The presenting sponsor of today's episode is Granola, the AI notepad that helps you get the doing done. Whether it's identifying to-do items after a call, turning a brainstorming section into a product spec, or looking back at multiple calls to identify cultural trends at your company, Granola takes your raw meeting notes and makes them awesome. Right now, Granola is featuring AI recipes from AI thought leaders, including several past guests of this show. My own contribution is a blind spot finder recipe that looks back at recent conversations and attempts to identify things that I am totally missing. This was immediately useful in the context of contingency planning for my son's cancer treatment. And the more data Granola collects as I continue to use it, the more valuable it becomes for suggesting AI topic areas that I really ought to explore. See the link in our show notes to try my blind spot finder recipe and experience for yourself how Granola puts your meetings to work. Now today I'm excited to share a special combined crossover episode featuring Olive Song, a senior researcher specializing in reinforcement learning and model evaluation at the Chinese AI company Minimax. Creators of the M series of models, the most recent of which, M25, currently tops the Open Router Usage Leaderboard.
To give you the most complete picture possible, we're combining two sources. First, a presentation Olive recently gave at the AI Engineer Conference in New York, where she had previously lived for six years. And second, an interview with Ksenia Se from her podcast, Inference by Turing Post. Together, they provide an excellent overview of Minimax's goals as a company, the capabilities they're prioritizing in their models, the techniques they're using to get there, and the day-to-day ups and downs of training frontier LLMs. Highlights include how Minimax's strategy of building both models and user-facing applications in-house creates tight feedback loops that enable their cross-functional research and engineering teams to identify and address model weaknesses as quickly as possible. An overview of how interleaved thinking, which allows the model to take an action, get feedback from the environment, and pause to think again before continuing, improves performance on long-horizon, agentic tasks. A description of the perturbation pipeline they use to systematically vary the model's training environment in order to encourage robust generalization. Olive's perspective on the constant battle she and teammates are fighting against reward hacking, a window into the tedious debugging that is sometimes required to diagnose training issues and how they realized that they needed to run reinforcement learning at FP32 precision. And finally, how the team at Minimax is using AI agents to keep up with the daily flood of AI news. While Olive recognizes that Minimax's models, like all open-source models in the world today, can't quite match the performance of top American models, I think there is still a lot of value in the details she shares about their approach to reinforcement learning and how they structure their team and work. And in any case, I always appreciate the opportunity to hear directly from Chinese AI researchers, who, just like their American counterparts, are figuring things out step by step as they go, even as major questions about issues such as the governance of increasingly powerful open-source models remain fundamentally unanswered. With that, I want to thank Swix, the creator of the AI Engineer Event series, which I absolutely recommend attending if you can, and Ksenia, the creator of Turing Post, which has what I find to be some of the very best topic selection of any AI newsletter, for allowing me to create and post this combined episode. And I hope you enjoy this window into the development of some of the best open-weight models in the world, with Olive Song of Minimax.

**Olive Song** (4:16)
Hi, hi, everyone. I'm Olive. It's my great honor here today to present on our new model, Minimax M2. I actually lived in New York City for six years, so it feels great to come back, but with a different role. I currently study reinforcement learning and model evaluation at Minimax. Let me just get a quick sense of the room. Who here has heard or have tried of Minimax before? Oh, a couple of there, yeah. Not everybody, but I guess, yeah. But here's the value of me standing here today. So we are a global company that works on both foundation models and applications. We develop multimodality models, including text, vision language models, our video generation model, HALO, and speech generation, music generation stuff. And we also have many applications, including agents and stuff in house. So that's the specific thing that's different from the other labs or other companies. So we both develop foundation models and applications. So we have research and developers sitting side by side working on things. So our difference would be that we have firsthand experience from our in-house developers into developing models that developers would really need in the community. And here I want to introduce our Minimax M2, which is an open-weight model, very small, with only 10 billion active parameters, that was designed specifically for coding workplace agentic tasks. It's very cost efficient.