---
license: mit
library_name: transformers
pipeline_tag: text-generation
---

This is the model is trained using paper, [M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models](https://arxiv.org/abs/2504.10449).


| **Model**                          | **AIME 2025** | **AIME 2024** | **MATH 500** | **AMC 2023** | **OlympiadBench** |
|-----------------------------------|---------------|---------------|--------------|--------------|-------------------|
| Qwen2.5-Math-7B-Instruct  (Transformer)        | –             | 13.3          | 79.8         | 50.6         | 40.7              |
| rStar-Math-7B  (Transformer)                   | –             | 26.7          | 78.4         | 47.5         | 47.1              |
| Eurus-2-7B-PRIME (Transformer)                 | –             | 26.7          | 79.2         | 57.8         | 42.1              |
| Qwen2.5-7B-SimpleRL (Transformer)              | –             | 26.7          | 82.4         | 62.5         | 43.3              |
| DeepSeek-R1-Distill-Qwen-1.5B (Transformer)    | 23.0          | 28.8          | 82.8         | 62.9         | 43.3              |
| **M1-3B (Mamba Hybrid Models)**                | 23.5          | 28.5          | 84.0         | 62.8         | 47.3              |



Code: https://github.com/jxiw/M1

```
@article{wang2025m1scalabletesttimecompute,
  title={M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models}, 
  author={Junxiong Wang and Wen-Ding Li and Daniele Paliotta and Daniel Ritter and Alexander M. Rush and Tri Dao},
  journal={arXiv preprint arXiv:2504.10449},
  year={2025},
  url={https://arxiv.org/abs/2504.10449}, 
}