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--- |
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license: mit |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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This is the model is trained using paper, [M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models](https://arxiv.org/abs/2504.10449). |
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| **Model** | **AIME 2025** | **AIME 2024** | **MATH 500** | **AMC 2023** | **OlympiadBench** | |
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|-----------------------------------|---------------|---------------|--------------|--------------|-------------------| |
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| Qwen2.5-Math-7B-Instruct (Transformer) | – | 13.3 | 79.8 | 50.6 | 40.7 | |
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| rStar-Math-7B (Transformer) | – | 26.7 | 78.4 | 47.5 | 47.1 | |
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| Eurus-2-7B-PRIME (Transformer) | – | 26.7 | 79.2 | 57.8 | 42.1 | |
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| Qwen2.5-7B-SimpleRL (Transformer) | – | 26.7 | 82.4 | 62.5 | 43.3 | |
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| DeepSeek-R1-Distill-Qwen-1.5B (Transformer) | 23.0 | 28.8 | 82.8 | 62.9 | 43.3 | |
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| **M1-3B (Mamba Hybrid Models)** | 23.5 | 28.5 | 84.0 | 62.8 | 47.3 | |
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Code: https://github.com/jxiw/M1 |
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``` |
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@article{wang2025m1scalabletesttimecompute, |
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title={M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models}, |
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author={Junxiong Wang and Wen-Ding Li and Daniele Paliotta and Daniel Ritter and Alexander M. Rush and Tri Dao}, |
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journal={arXiv preprint arXiv:2504.10449}, |
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year={2025}, |
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url={https://arxiv.org/abs/2504.10449}, |
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} |
