FreedomIntelligence/OnePO-Medical-20K

<div align="center"> <h1>OnePO-Medical-20K</h1> <a href="https://openreview.net/pdf?id=M8eyUQldfx">📄 Paper</a> | <a href="https://github.com/FreedomIntelligence/HuatuoGPT-3">💻 GitHub</a> </div>

⚡ Introduction

OnePO-Medical-20K is the medical RL dataset released with OnePO, containing 20,338 medical tasks across multiple languages.

• —One stage, no preceding SFT. OnePO adapts pretrained models to medicine through a single reinforcement-learning stage.
• —Two complementary task types. Multiple-choice questions provide verifiable answers. Open-ended conversations provide scoring rubrics.
• —Teacher guidance included. Each task includes a HuatuoGPT-3 response for guidance during training.

📚 What's inside

Data file: onepo_medical_20K.json — a JSON array with one record per task.

Fields that do not apply to a task are null. Teacher responses can be replaced with outputs from another teacher model.

🚀 Usage

Load directly from Hugging Face:

from datasets import load_dataset

dataset = load_dataset("FreedomIntelligence/OnePO-Medical-20K", split="train")
print(dataset[0])

To train with OnePO, follow the repository setup, then point TRAIN_FILE to the downloaded JSON:

TRAIN_FILE=/path/to/onepo_medical_20K.json bash OnePO.sh

📖 Citation

@inproceedings{chen2026onepo,
  title={OnePO: Direct One-stage Policy Optimization for SFT-free Domain Adaptation},
  author={Chen, Junying and Xie, Xinyuan and Li, Ziniu and Wang, Benyou},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning},
  year={2026}
}