Datasets:

FreedomIntelligence
/

OnePO-Medical-20K

task_categories:
  - question-answering
  - text-generation
size_categories:
  - 10K<n<100K
tags:
  - medical
  - reinforcement-learning
  - onepo
  - huatuogpt-3
configs:
  - config_name: default
    data_files:
      - split: train
        path: onepo_medical_20K.json

OnePO-Medical-20K

📄 Paper | 💻 GitHub

⚡ 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

Task type	Samples	How responses are scored
Multiple-choice	10,191	Match the selected option against the correct answer.
Open-ended	10,147	Assess the response against weighted rubric criteria.

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

Fields	Description
`id`, `type`	Unique ID and task type: `multiple_choice` or `open_ended`.
`question`, `options`, `answer_idx`	Multiple-choice question, labeled options, and correct option label.
`prompt`, `rubrics`	Open-ended conversation and scoring criteria, each with `criterion` and signed `points`.
`teacher_response`	Teacher-generated response for training guidance, with reasoning in `<think>...</think>` followed by the final answer.

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}
}