> Code Data For coding problems, we curate a high-quality training set comprising open-source datasets and our newly collected problem set. We remove problems without test cases. For problems with golden solutions, we exclude those where the golden solution failed to pass all test cases. For problems without golden solution, we discard problems where no test case can be solved in 16 rollouts of advanced reasoning models. Similar to math data, we utilize an SFT version of MiMo-7B to filter out easy problems that are perfectly solved in all 16 rollouts. This rigorous cleaning process yields 30K code problems.
> During each RL iteration, we evaluate thousands of problems to compute the rewards, with each problem potentially containing hundreds of test cases. To improve reward computing efficiency and eliminate GPU idle time, we developed an online judge environment that enables parallel execution of extremely high-volume unit tests.
I've become pretty skeptical about eval results given what we've heard about llama4 so we'll see where this lands on the closed evals but very impressive to see.
https://github.com/ollama/ollama/blob/main/docs%2Fmodelfile....
That moe strikes me as the better overall tradeoff
A couple things stand out to me — first is that the 7B model is trained on 25T tokens(!). This is Meta-scale training; Llama 4 Maverick was trained on 22T or so. (Scout, the smaller model: 40T).
Second, this is an interesting path to take - not a distilled model or an RL layer to get reasoning out of another model, but a from-scratch RL model with reasoning baked in; the claims seem to indicate you get a lot of extra efficiency per-parameter doing this.
I don’t have experience with Xiaomi models, so I’m cautious about this one until I play with it, but it looks like a super viable local reasoning model from the stats.
They could've called it Xiaomimo.
Probably within few hours will be released.
Here is the meaning of the name
Described here: https://finance.sina.cn/tech/2020-11-26/detail-iiznctke33979...
在后来的讨论中，我突然想到了我最喜欢的一句话——“佛观一粒米，大如须弥山”。
Translated into English, it means:
“In the later discussions, I suddenly thought of one of my favorite sayings — ‘A Buddha sees a single grain of rice as vast as Mount Sumeru.’”
This expression emphasizes the idea that even something seemingly small (like a grain of rice) can hold immense significance or value when viewed from a different perspective.
Thanks to chatgpt for translating this