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+.DS_Store

README.md

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+---
+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
+---
+
+<div align="center">
+<h1>PubMedVision-RL</h1>
+<a href="https://openreview.net/pdf?id=0BXKsjKlFJ">📄 Paper</a> |
+<a href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision-2">💻 GitHub</a>
+</div>
+
+## ⚡ 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](https://huggingface.co/datasets/FreedomIntelligence/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:
+
+```python
+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
+
+```bibtex
+@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}
+}
+```