---
license: apache-2.0
---


## MUR: Momentum Uncertainty guided Reasoning for Large Language Models

Paper Link: https://huggingface.co/papers/2507.14958

Code Repo: https://github.com/yayayacc/MUR



## 🔥 News

- 🔥🔥🔥 We release the paper as well as the code for MUR ! ! !


## 📖 Results

MUR reduces computation by over 50\% on average across three backbone models, while improving accuracy by 0.62–3.37\%.

<p align="center">
    <img src="Intro.png" alt="scaling" width="400">
</p>

## 🚀 Quick Start

To use MUR, we can try with the following command.

Firstly, create the environment and install the requirements. This implementation is accelerated and supported by vllm.

```bash
# env
conda create -n mur python==3.11.9
conda activate mur
pip install -r requirements.txt
```

Next, simply run different python files: 

```python
python [TTS setting]-[vanilla|mur].py
```

Finally, run eval files. To be specific, please eval gpqa_diamond dataset using ``eval/eval_gpqa_cot.py``. Adiitionaly, use ``eval/math_verifier.py`` to verify math datasets.

Feel free to contact with me if you have any questions ~~~

## Citation
If you find it helpful, please kindly cite the paper.
```
@article{yan2025mur,
  title={MUR: Momentum Uncertainty guided Reasoning for Large Language Models},
  author={Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Luu Anh Tuan, Haiteng Zhao, Qika Lin, Jun Liu},
  journal={arXiv preprint arXiv:2507.14958},
  year={2025}
}
```