Well, it seems like the open-source AI landscape just got a bit more crowded – and perhaps more interesting. Xiaomi has officially thrown its hat into the ring by introducing MiMo. Now, this isn’t just another large language model; apparently, Xiaomi’s aiming specifically at improving reasoning capabilities with this one. That definitely catches my attention.
This new model comes courtesy of a newly formed group within the company, the “Xiaomi Big Model Core Team.” MiMo itself is what they call a 7-billion-parameter model. In the grand scheme of things, that’s not massive compared to some of the behemoths out there. But here’s the interesting claim: Xiaomi says MiMo really punches above its weight class, particularly when it comes to mathematical reasoning and generating code. They’re suggesting it performs on par with significantly larger models, even mentioning names like OpenAI’s o1-mini and a preview of Alibaba’s 32-billion-parameter Qwen.
Getting that kind of reasoning power out of a smaller model isn’t easy, and Xiaomi acknowledges this. Typically, the really impressive results we see, especially from reinforcement learning techniques, come from much bigger architectures. So, what’s their secret sauce, supposedly? They believe it boils down to maximizing the potential hidden within that base 7B model. This apparently involved some very deliberate strategies during both the pre-training and post-training phases. And, of course, a potential advantage of keeping the model relatively small is its usability – maybe for businesses that don’t have massive GPU clusters, or perhaps even for running on edge devices with limited resources down the line.
How Did They Build It? A Peek Under the Hood
Okay, so how did they actually try to instill this reasoning prowess? Things get a bit technical here, but let’s try to break down their approach.
Sharpening the Mind: Pre-Training Focus
The foundation seems to be a heavily optimized pre-training process. Xiaomi mentions they really worked on their data handling – improving how they process raw data, enhancing the tools they use to extract relevant text, and using multiple layers of filtering. The goal? To increase the density of reasoning patterns within the training material. It sounds like they weren’t just throwing data at it, but carefully curating it.
They put together a specialized dataset containing around 200 billion ‘reasoning tokens’ (think of tokens as pieces of words or code). Then, they applied a three-stage data mixing strategy, training the model progressively over three phases on a staggering 25 trillion tokens in total. That’s a lot of learning! They also employed a technique called Multiple-Token Prediction, which they claim not only boosted the model’s performance but also helps it generate responses faster later on.
Refining the Skills: Post-Training with RL
After the initial build, they moved into fine-tuning using reinforcement learning (RL). This involved feeding MiMo around 130,000 math and coding problems. Importantly, these problems were verified for accuracy and difficulty using rule-based systems – trying to ensure the model learned from good examples.
Now, RL can be tricky with complex problems where correct answers (and thus rewards) are few and far between (what researchers call ‘sparse rewards’). To get around this, the Xiaomi team implemented a couple of clever tricks. One is a “Test Difficulty Driven Reward” system – which I think means the reward adjusts based on how tough the problem is. The other is “Easy Data Re-Sampling,” seemingly a way to keep the RL training stable by revisiting easier problems effectively.
Speeding Things Up
Training these massive models takes serious time and computational power. To help with that, Xiaomi developed something they call a “Seamless Rollout Engine.” The aim here was to cut down on GPU downtime during the training and validation cycles. And the results they’re reporting are pretty eye-catching: a claimed 2.29x speedup in training and a 1.96x boost in validation speed. Getting things done faster is always a huge plus in AI development. This engine apparently also supports that Multiple-Token Prediction technique within a popular framework (vLLM) and generally makes their RL system’s inference more stable.
Different Flavors of MiMo
Xiaomi isn’t just releasing one version. The MiMo-7B series actually includes four variants you can check out:
- MiMo-7B-Base: The foundational model, said to have strong reasoning potential.
- MiMo-7B-RL-Zero: An RL model trained directly from that base version.
- MiMo-7B-SFT: A version created using supervised fine-tuning (showing it examples).
- MiMo-7B-RL: This seems to be the top performer. It’s an RL model trained starting from the SFT version, and it’s the one Xiaomi benchmarks against others like OpenAI’s o1-mini.
So, How Does It Actually Perform?
Xiaomi shared a bunch of benchmark scores for the MiMo-7B-RL variant (tested with a specific setting, temperature = 0.6). Benchmarks are just one piece of the puzzle, of course, but they give us an idea:
- Mathematics:
- MATH-500: Hits 95.8% accuracy on the first try (Pass@1) in a single run. That looks very strong.
- AIME 2024 (a tough math competition): Averaged 68.2% Pass@1 over 32 runs.
- AIME 2025: Averaged 55.4% Pass@1 over 32 runs.
- Code Generation:
- LiveCodeBench v5: 57.8% Pass@1 (avg. 8 runs).
- LiveCodeBench v6: 49.3% Pass@1 (avg. 8 runs). Decent scores here.
- General Reasoning/Tasks:
- GPQA Diamond: 54.4% Pass@1 (avg. 8 runs).
- SuperGPQA: 40.5% Pass@1 (single run).
- DROP (Reading Comprehension, F1 score): 78.7.
- MMLU-Pro (Broad knowledge, Exact Match): 58.6.
- IF-Eval (Instruction Following): 61.0 (avg. 8 runs).
Looking at these numbers, particularly the math results, MiMo certainly seems capable for its size. The coding and general task performance appears competitive too.
Where Can You Find MiMo?
Maybe the best news for developers and researchers is the accessibility. Xiaomi has made the entire MiMo-7B model series open-source. You can find the models ready to download and use on Hugging Face. If you want to dive deeper into the technical details, they’ve also published a full report and the model checkpoints over on GitHub. It’s genuinely good to see another major tech company contributing potentially powerful tools back to the wider community. We’ll have to see how people start using MiMo in the real world!
