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







## 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}
}
```

# 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 是一款用于评测过程奖励模型（Process Reward Model，PRM）与多模态大语言模型（Multimodal Large Language Model，MLLM）在多模态推理任务中识别错误步骤能力的评测基准。该基准集包含2866个样本，共计26950条人工标注的逐步骤正确性标签。

## 数据字段
- 单样本数据字段：

| 键名 | 描述 |
| -------------- | ------------------------------------------------------------------------------------------ |
| `image` | 图像路径列表。 |
| `question` | 输入查询。 |
| `answer` | 该问题的标准答案。 |
| `response` | 模型针对该问题生成的回答，已拆分为多个步骤。 |
| `policy_model` | 用于生成该回答的模型。 |
| `data_source` | 该问题的来源。 |

- 单回答数据字段：

| 键名 | 描述 |
| --------------------- | -------------------------------------------------------------------------------------------------- |
| `steps` | 该回答的步骤序列。 |
| `process_correctness` | 各步骤的正确性标注，其中1、0、-1分别代表正确、中性与错误。 |

## 数据示例







## 许可证

本项目基于MIT许可证发布。本项目使用了预训练的internlm2_5-7b-chat作为组件，该组件遵循Apache License 2.0许可证。

## 引用

若您在研究中使用本项目并认为其具有参考价值，请考虑引用以下文献：

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}
}

搜集汇总

背景与挑战

背景概述

VisualProcessBench是一个用于评估过程奖励模型和多模态大语言模型在多模态推理任务中识别错误步骤能力的基准数据集。该数据集包含2,866个样本，提供了26,950个人工标注的步骤级正确性标签，适用于模型性能验证和评估。