Update README.md

README.md

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----
-license: mit
----
+---
+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
+---
+
+# Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
+
+**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.
+
+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      |
+
+
+
+## 👥 Human Evaluation
+
+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.
+
+![](assets/human.png)
+
+
+
+## 🔬 Ablation Studies
+
+Our ablation studies validate the critical contributions of our framework's key components.
+
+***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.
+
+<p align="center">
+  <img src="assets/radar_exp.jpg" style="width:60%;" />
+</p>
+
+***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.
+
+![](assets/patient_scaling.png)
+
+
+
+
+## 📜 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}, 
+}
+
+```
+
+
+
+## 💬 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**.
+
+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. 🤝
+
+