VisualPRM400K

[📂 GitHub] [📜 Paper] [🆕 Blog] [🤗 model] [🤗 dataset] [🤗 benchmark]

2025/04/11: We release a new version of VisualPRM400K (i.e., VisualPRM400K-v1.1), which includes additional data sources to enhance the data diversity.

VisualPRM400K is a dataset comprising approximately 400K multimodal process supervision data. We generate the data using an automatic data pipeline. The key idea is to estimate the expected accuracy \(mc_i\) of the given step \(s_{\leq i}\) based on Monte Carlo sampling and consider the step correct if \(mc_i>0\). Please see our paper or blog for more details.

NOTE: This dataset is formulated as multi-turn conversation and the expected accuracy \(mc_i\) has been converted into correctness token \(c_i \in {+,-}\). If you want to use the annotations for expected accuracy, please refer to this version.

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:

@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}
}