GM-PRM-20K

Training dataset for GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning (arXiv:2508.04088).

Accepted at the 4th Workshop on Advances in Language and Vision Research (ALVR), in conjunction with ACL 2026 (San Diego, California, July 2026).

This is the exact SFT dataset used to train the released model zijinghuafen/GM-PRM.

What it is

Each sample is a full multi-step solution to a multimodal math problem, paired with a generative critique that, for every step, analyzes step intent, image alignment, and reasoning logic, gives a per-step judgement, and — for the first incorrect step — produces a corrected version. Critiques were generated with GPT-4o and filtered by jointly requiring agreement between GPT-4o (LLM-as-a-judge) and Monte-Carlo estimation.

Statistics

• 19,614 samples total
  • 9,061 solutions containing an incorrect step (with a corrected step)
  • 10,553 solutions in which all steps are judged correct
• Problem types: plane geometry and functions (the geometry+function subset of VisualPRM400K)

Format

GM-PRM-20K.json — a JSON list in ms-swift SFT format:

{
  "messages": [
    {"role": "user", "content": "...task instructions + solution steps..."},
    {"role": "assistant", "content": "...per-step analysis + judgements + corrected step..."}
  ],
  "images": ["images/MAVIS-Geometry/RuleBaseGeo/depth0/49304/49305_vision_dominant.jpg"]
}

Images (not bundled)

To keep this release lightweight and respect upstream licensing, images are not included. The images paths are relative to the image root of VisualPRM400K. To use this dataset:

1. Download the images from OpenGVLab/VisualPRM400K.
2. Point the relative paths (e.g. images/...) at your local VisualPRM400K image directory.

The 8 source collections referenced: GEOS, Geo170K, GeomVerse, Geometry3K, MAVIS-Function, MAVIS-Geometry, UniGeo, geoqa_plus.

License

The textual critiques in this dataset were generated by GPT-4o. The underlying problems and images originate from VisualPRM400K and its source datasets — please follow their respective licenses when using the images. Set the license field above to match your intended terms before making the dataset public.

Citation

@inproceedings{zhang2026gmprm,
  title={GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning},
  author={Zhang, Jianghangfan and Yan, Yibo and Zheng, Kening and Zou, Xin and Dai, Song and Hu, Xuming},
  booktitle={Proceedings of the 4th Workshop on Advances in Language and Vision Research (ALVR), in conjunction with ACL 2026},
  month={July},
  year={2026},
  address={San Diego, California, USA},
  publisher={Association for Computational Linguistics}
}