---
title: "Xiaomi surged about 3% during the session, announcing the open-source of its first inference large model \"Xiaomi MiMo\""
type: "News"
locale: "zh-HK"
url: "https://longbridge.com/zh-HK/news/238185824.md"
description: "Xiaomi Group surged during the trading session, currently up about 3%. On the news front, Xiaomi announced today the open-sourcing of its first large model designed for inference, \"Xiaomi MiMo,\" which integrates pre-training and post-training to comprehensively enhance inference capabilities"
datetime: "2025-04-30T02:55:48.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/238185824.md)
  - [en](https://longbridge.com/en/news/238185824.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/238185824.md)
---

> 支持的語言: [简体中文](https://longbridge.com/zh-CN/news/238185824.md) | [English](https://longbridge.com/en/news/238185824.md)


# Xiaomi surged about 3% during the session, announcing the open-source of its first inference large model "Xiaomi MiMo"

Today, Xiaomi open-sourced its first large model designed for reasoning, "Xiaomi MiMo," linking pre-training to post-training to comprehensively enhance reasoning capabilities.

In the public evaluation sets for mathematical reasoning (AIME 24-25) and coding competitions (LiveCodeBench v5), MiMo, with only 7B parameters, surpassed OpenAI's closed-source reasoning model o1-mini and Alibaba's larger open-source reasoning model QwQ-32B-Preview.

The enhancement of MiMo's reasoning capabilities is driven by innovations in data and algorithms across multiple dimensions during the pre-training and post-training phases, including:

**Pre-training: The core is to expose the model to more reasoning patterns**

-   Data: Focused on mining rich reasoning corpora and synthesizing approximately 200B tokens of reasoning data.
-   Training: Conducted three-stage training, gradually increasing training difficulty, totaling 25T tokens of training.

**Post-training: The core is an efficient and stable reinforcement learning algorithm and framework**

-   Algorithm: Proposed Test Difficulty Driven Reward to alleviate the reward sparsity problem in difficult algorithm issues and introduced Easy Data Re-Sampling strategy to stabilize RL training.
-   Framework: Designed the Seamless Rollout system, accelerating RL training by 2.29 times and validation by 1.96 times.

MiMo-7B has open-sourced 4 models to HuggingFace: https://huggingface.co/XiaomiMiMo

Technical details can be found in the technical report: \[https://github.com/XiaomiMiMo/MiMo/blob/main/MiMo-7B-Technical-Report.pdf\](https://www.oschina.net/action/GoToLink?url=https%3A%2F%2Fgithub.com%2FXiaomiMiMo%2FMiMo%2Fblob%2Fmain%2FMiMo-7B-Technical-Report.pdf)

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