| --- |
| 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 |
| --- |
| |
| <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 |
|
|
| | 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: |
|
|
| ```python |
| 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](https://github.com/FreedomIntelligence/HuatuoGPT-3), then point `TRAIN_FILE` to the downloaded JSON: |
|
|
| ```bash |
| TRAIN_FILE=/path/to/onepo_medical_20K.json bash OnePO.sh |
| ``` |
|
|
| ## 📖 Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
