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--- |
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license: mit |
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language: en |
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pipeline_tag: text-generation |
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tags: |
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- conversational |
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- text-generation |
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- medical |
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- diagnosis |
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- agent |
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- reinforcement-learning |
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base_model: Qwen3-8B |
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datasets: |
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- HealthBench |
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- MAQuE |
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- MedQA |
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- MMLU |
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paper: 2510.04284 |
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model_name: Doctor-R1 |
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metrics: |
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- accuracy |
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--- |
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# 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. |
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## ✨ Key Features |
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* **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. |
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* **Experiential Reinforcement Learning:** A novel closed-loop framework where the agent learns and improves from an accumulating repository of its own high-quality experiences. |
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* **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. |
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* **State-of-the-Art Performance:** Outperforms leading open-source models on challenging dynamic benchmarks like HealthBench and MAQuE with high parameter efficiency (8B). |
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## 🏆 Leaderboards |
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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. |
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| Benchmark | Key Metric | Doctor-R1 | Best Open-Source (>=32B) | |
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| :----------------- | :--------- | :-------: | :----------------------: | |
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| **HealthBench** | Avg. Score | **36.29** | 33.16 | |
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| **MAQuE** | Accuracy | **60.00** | 57.00 | |
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| **MedQA** | Accuracy | **83.50** | 81.50 | |
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| **MMLU (Medical)** | Accuracy | **85.00** | 84.00 | |
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The detailed breakdown of **HealthBench Main (Dynamic Consultation)** is as below: |
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| Model | Avg. Score | Accuracy | Comm. Quality | Context Aware. | |
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| :------------------------ | :--------: | :-------: | :-----------: | :------------: | |
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| **GPT-o3** (Proprietary) | 38.91 | 40.31 | 64.78 | 48.09 | |
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| **Doctor-R1 (8B)** | **36.29** | **37.84** | **64.15** | **49.24** | |
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| Baichuan-M2-32B | 33.16 | 33.95 | 58.01 | 46.80 | |
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| Grok-4 (Proprietary) | 33.03 | 37.95 | 61.35 | 45.62 | |
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| GPT-4.1 (Proprietary) | 31.18 | 34.78 | 60.65 | 44.81 | |
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| UltraMedical-8B | 22.19 | 25.50 | 57.40 | 40.26 | |
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| **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"> |
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<img src="assets/radar_exp.jpg" style="width:60%;" /> |
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</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 |
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If you find our work useful in your research, please consider citing our paper: |
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```bibtex |
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@misc{lai2025doctorr1masteringclinicalinquiry, |
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title={Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning}, |
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author={Yunghwei Lai and Kaiming Liu and Ziyue Wang and Weizhi Ma and Yang Liu}, |
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year={2025}, |
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eprint={2510.04284}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2510.04284}, |
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} |
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``` |
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## 💬 Contact & Questions |
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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. 🤝 |
