---
title: VisualProcessBench
canonical_url: "https://www.modelscope.cn/datasets/OpenGVLab/VisualProcessBench"
md_url: "https://www.modelscope.cn/datasets/OpenGVLab/VisualProcessBench.md"
repository: OpenGVLab/VisualProcessBench
last_updated: 2025-03-18
license: "Apache License 2.0"
storage_size: "487 MB"
downloads: 992
stars: 0
---

# VisualProcessBench

> VisualProcessBench - OpenGVLab 在 ModelScope 开源的数据集。[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 Paper\]](https://arxiv.org/abs/2503.10291) [\[🆕 Blog\]](https://internvl.github.io/blog/2025-03-13-VisualPRM/) [\[🤗…

OpenGVLab/VisualProcessBench 是 ModelScope 魔搭社区上的数据集，存储大小 487 MB，采用 Apache License 2.0 许可。

- **Repository**: OpenGVLab/VisualProcessBench
- **License**: Apache License 2.0
- **Storage size**: 487 MB
- **Downloads**: 992
- **Stars**: 0
- **Last updated**: 2025-03-18

Source: https://www.modelscope.cn/datasets/OpenGVLab/VisualProcessBench

---

# VisualProcessBench

[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)
[\[📜 Paper\]](https://arxiv.org/abs/2503.10291)
[\[🆕 Blog\]](https://internvl.github.io/blog/2025-03-13-VisualPRM/)
[\[🤗 model\]](https://huggingface.co/OpenGVLab/VisualPRM-8B)
[\[🤗 dataset\]](https://huggingface.co/datasets/OpenGVLab/VisualPRM400K)
[\[🤗 benchmark\]](https://huggingface.co/datasets/OpenGVLab/VisualProcessBench)

VisualProcessBench is a benchmark designed to measure the abilities of PRMs and MLLMs to identify erroneous steps in multimodal reasoning tasks. This benchmark comprises 2,866 samples with a total of 26,950 human-annotated step-wise correctness labels.

## Data fields
- Data fields for each sample:
  
    | Key            | Description                                                                                |
    | -------------- | ------------------------------------------------------------------------------------------ |
    | `image`        | List of Image path.                                                                        |
    | `question`     | Input query.                                                                               |
    | `answer`       | Ground Truth to this question.                                                             |
    | `response`     | The model-generated response to this question, which has been splited into multiple steps. |
    | `policy_model` | The model used to generate the response.                                                   |
    | `data_source`  | The source of this question.                                                               |

- Data fields for each response:

    | Key                   | Description                                                                                        |
    | --------------------- | -------------------------------------------------------------------------------------------------- |
    | `steps`               | Steps of this response.                                                                            |
    | `process_correctness` | Correctness annotation of each step. 1, 0, -1 denotes correct, neural, and incorrect, respectively |


## Data Examples

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/example-1.png?raw=true)

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mmmu-1.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mmmu-2.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mmmu-3.png?raw=true)

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathverse-1.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathverse-2.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathverse-3.png?raw=true)

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/DynaMath-1.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/DynaMath-2.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/DynaMath-3.png?raw=true)

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathvision-1.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathvision-2.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/mathvision-3.png?raw=true)

![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/wemath-1.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/wemath-2.png?raw=true)
![image/png](https://github.com/InternVL/InternVL.github.io/blob/main/blog/2025-03-13-VisualPRM/images/benchmark-examples/wemath-3.png?raw=true)

## License

This project is released under the MIT License. This project uses the pre-trained internlm2_5-7b-chat as a component, which is licensed under the Apache License 2.0.

## Citation

If you find this project useful in your research, please consider citing:

```BibTeX
@article{wang2025visualprm,
  title={VisualPRM: An Effective Process Reward Model for Multimodal Reasoning},
  author={Wang, Weiyun and Gao, Zhangwei and Chen, Lianjie and Chen, Zhe and Zhu, Jinguo and Zhao, Xiangyu and Liu, Yangzhou and Cao, Yue and Ye, Shenglong and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2503.10291},
  year={2025}
}
```