Datasets:
metadata
language:
- en
task_categories:
- visual-question-answering
size_categories:
- 10K<n<100K
tags:
- medical
- multimodal
- reinforcement-learning
- huatuogpt-vision-2
configs:
- config_name: open_ended
data_files:
- split: train
path: data/open_ended/*.jsonl
- config_name: multiple_choice
data_files:
- split: train
path: data/multiple_choice/*.jsonl
⚡ Introduction
PubMedVision-RL is a 70K medical multimodal RL dataset for eliciting medical vision capabilities in VLMs. It is the RL data behind HuatuoGPT-Vision-2.
- 🩺 Built from real medical data. Every question comes from a real PubMed image-caption pair in PubMedVision-Plus, grounded in expert-written captions.
- 🎯 Two complementary types. Multiple-choice questions with verifiable labels, and open-ended questions with weighted rubrics.
- ✅ Quality filtered. A second GPT-5.4 pass validates every question and rubric. Ambiguous items and those going beyond the caption are removed.
- 🔥 Difficulty filtered. Questions that Qwen3-VL-8B-Instruct already solves are dropped, so each sample gives a useful RL signal.
📚 What's inside
| Configuration | Samples | Reward |
|---|---|---|
multiple_choice |
35,331 | Verifiable (option label) |
open_ended |
35,154 | Weighted rubrics |
Both configurations use the train split and include the question, image paths, and source caption.
🚀 Usage
After downloading the repository:
from datasets import load_dataset
root = "/path/to/PubMedVision-RL"
open_ended = load_dataset(root, "open_ended", split="train")
multiple_choice = load_dataset(root, "multiple_choice", split="train")
print(open_ended[0])
Image paths are relative to the repository root. Provide the question and images to the model. Use rubrics or answer labels to compute rewards. Captions are retained for reference.
📖 Citation
@inproceedings{chen2026incentivizing,
title={Incentivizing Medical Vision Capabilities from Large-Scale Multimodal Pre-training},
author={Chen, Junying and Cai, Zhenyang and Yang, Yunjin and Cai, Kunyuan and Wang, Rongsheng and Wang, Xidong and Yu, Geyang and Feng, Xiangyi and Wang, Benyou},
booktitle={The Fortieth Annual Conference on Neural Information Processing Systems},
year={2026}
}