| | --- |
| | license: mit |
| | language: en |
| | pipeline_tag: text-generation |
| | tags: |
| | - conversational |
| | - text-generation |
| | - medical |
| | - diagnosis |
| | - agent |
| | - reinforcement-learning |
| | base_model: Qwen3-8B |
| | datasets: |
| | - HealthBench |
| | - MAQuE |
| | - MedQA |
| | - MMLU |
| | paper: 2510.04284 |
| | model_name: Doctor-R1 |
| | metrics: |
| | - accuracy |
| | --- |
| | |
| | # Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning |
| |
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| | **Doctor-R1** is an AI doctor agent trained to conduct strategic, multi-turn patient inquiries to guide its diagnostic decision-making. Unlike traditional models that excel at static medical QA, Doctor-R1 is designed to master the complete, dynamic consultation process, unifying the two core skills of a human physician: communication and decision-making. |
| |
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| | This model is an 8B parameter agent built upon **Qwen3-8B** and fine-tuned using a novel **Experiential Agentic Reinforcement Learning** framework. |
| |
|
| | ## ✨ Key Features |
| |
|
| | * **Unified Clinical Skills:** The first agent framework to holistically integrate two core clinical skills, **strategic patient inquiry** and **accurate medical decision-making** within a single model. |
| | * **Experiential Reinforcement Learning:** A novel closed-loop framework where the agent learns and improves from an accumulating repository of its own high-quality experiences. |
| | * **Dual-Competency Reward System:** A sophisticated two-tiered reward architecture that separately optimizes for both conversational quality (soft skills) and diagnostic accuracy (hard skills), featuring a "safety-first" veto system. |
| | * **State-of-the-Art Performance:** Outperforms leading open-source models on challenging dynamic benchmarks like HealthBench and MAQuE with high parameter efficiency (8B). |
| |
|
| | ## 🏆 Leaderboards |
| |
|
| | Doctor-R1 demonstrates state-of-the-art performance among open-source models and surpasses several powerful proprietary models on HealthBench. It demonstrates superior performance on dynamic benchmarks and strong foundational knowledge on static QA tasks. |
| |
|
| | | Benchmark | Key Metric | Doctor-R1 | Best Open-Source (>=32B) | |
| | | :----------------- | :--------- | :-------: | :----------------------: | |
| | | **HealthBench** | Avg. Score | **36.29** | 33.16 | |
| | | **MAQuE** | Accuracy | **60.00** | 57.00 | |
| | | **MedQA** | Accuracy | **83.50** | 81.50 | |
| | | **MMLU (Medical)** | Accuracy | **85.00** | 84.00 | |
| |
|
| | The detailed breakdown of **HealthBench Main (Dynamic Consultation)** is as below: |
| |
|
| | | Model | Avg. Score | Accuracy | Comm. Quality | Context Aware. | |
| | | :------------------------ | :--------: | :-------: | :-----------: | :------------: | |
| | | **GPT-o3** (Proprietary) | 38.91 | 40.31 | 64.78 | 48.09 | |
| | | **Doctor-R1 (8B)** | **36.29** | **37.84** | **64.15** | **49.24** | |
| | | Baichuan-M2-32B | 33.16 | 33.95 | 58.01 | 46.80 | |
| | | Grok-4 (Proprietary) | 33.03 | 37.95 | 61.35 | 45.62 | |
| | | GPT-4.1 (Proprietary) | 31.18 | 34.78 | 60.65 | 44.81 | |
| | | UltraMedical-8B | 22.19 | 25.50 | 57.40 | 40.26 | |
| | | **Base Model (Qwen3-8B)** | 25.13 | 28.57 | 49.35 | 43.00 | |
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| | ## 👥 Human Evaluation |
| |
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| | To validate that our quantitative results align with user experience, we conducted a pairwise human preference evaluation against other leading models. The results show a decisive preference for Doctor-R1, especially in patient-centric metrics. |
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| | ## 🔬 Ablation Studies |
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| | Our ablation studies validate the critical contributions of our framework's key components. |
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| | ***Impact of Experience Retrieval Mechanism.*** The results show that our full retrieval mechanism with reward and novelty filtering provides a significant performance boost over both a no-experience baseline and a standard similarity-based retrieval, especially in communication skills. |
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|
| | <p align="center"> |
| | <img src="assets/radar_exp.jpg" style="width:60%;" /> |
| | </p> |
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| | ***Impact of Patient Agent Scaling.*** We observe a strong, positive correlation between the number of simulated patient interactions during training and the agent's final performance. This validates that our agentic framework effectively learns and improves from a large volume of diverse experiences. |
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| | ## 📜 Citation |
| |
|
| | If you find our work useful in your research, please consider citing our paper: |
| |
|
| | ```bibtex |
| | @misc{lai2025doctorr1masteringclinicalinquiry, |
| | title={Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning}, |
| | author={Yunghwei Lai and Kaiming Liu and Ziyue Wang and Weizhi Ma and Yang Liu}, |
| | year={2025}, |
| | eprint={2510.04284}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.AI}, |
| | url={https://arxiv.org/abs/2510.04284}, |
| | } |
| | |
| | ``` |
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|
| | ## 💬 Contact & Questions |
| |
|
| | For collaborations or inquiries, please contact [**laiyunghwei@gmail.com**](mailto:laiyunghwei@gmail.com). You’re also welcome to open an issue or join the discussion in this repository, we value your insights and contributions to **Doctor-R1**. |
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| | Stay tuned and join our community as we push the boundaries of intelligent healthcare. Together, let’s make medical AI safer, smarter, and more human. 🤝 |
