MODEL PROFILE

MiMo-7B-RL

XiaomiMiMo · MiMo-7B-RL

Unlocking the Reasoning Potential of Language Model From Pretraining to Posttraining MATH500 (Pass@1) 95.8 97.2 AIME 2024 (Pass@1) 68.2 80.1 AIME 2025 (Pass@1) 55.4 70.2 Code LiveCodeBench v5 (Pass@1) 57.8 60.9 LiveCodeBench v6 (Pass@1) 49.3 52.2 STEM GPQA-Diamond (Pass@1) 54.4 60.6 General Alignbench1.1 (Evaluated by GPT4.1) 6.9 7.4 Currently, most successful RL works, including open-source research, rely on relatively large base models, e.g., 32B models, particularly for enhancing code reasoning capabilities. Moreover, it was widely considered that achieving uniform and simultaneous improvements in both mathematical and code capabilities within a small model is challenging. Nonetheless, we believe that the effectiveness of the RL trained reasoning model relies on the inherent reasoning potential of the base model. To fully unlock the reasoning potential of language models, efforts must focus not only on post-training but also on pre-training strategies tailored to reasoning.

Text generationlicense:mit
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 08:49:10
OVERVIEW

Model overview

Unlocking the Reasoning Potential of Language Model From Pretraining to Posttraining MATH500 (Pass@1) 95.8 97.2 AIME 2024 (Pass@1) 68.2 80.1 AIME 2025 (Pass@1) 55.4 70.2 Code LiveCodeBench v5 (Pass@1) 57.8 60.9 LiveCodeBench v6 (Pass@1) 49.3 52.2 STEM GPQA-Diamond (Pass@1) 54.4 60.6 General Alignbench1.1 (Evaluated by GPT4.1) 6.9 7.4 Currently, most successful RL works, including open-source research, rely on relatively large base models, e.g., 32B models, particularly for enhancing code reasoning capabilities. Moreover, it was widely considered that achieving uniform and simultaneous improvements in both mathematical and code capabilities within a small model is challenging. Nonetheless, we believe that the effectiveness of the RL trained reasoning model relies on the inherent reasoning potential of the base model. To fully unlock the reasoning potential of language models, efforts must focus not only on post-training but also on pre-training strategies tailored to reasoning.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。