# Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.15279 · HTML: https://mlsanity.com/paper/2509.15279/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 30 of 31)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding](https://mlsanity.com/paper/2501.18362/) (2025)
- [Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models](https://mlsanity.com/paper/2503.13939/) (2025)
- [Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset](https://mlsanity.com/paper/2306.03030/) (2023)
- [Baichuan-M2: Scaling Medical Capability with Large Verifier System](https://mlsanity.com/paper/2509.02208/) (2025)
- [CultureLLM: Incorporating Cultural Differences into Large Language Models](https://mlsanity.com/paper/2402.10946/) (2024)
- [Does Explainable Artificial Intelligence Improve Human Decision-Making?](https://mlsanity.com/paper/2006.11194/) (2020)
- [MedCoT: Medical Chain of Thought via Hierarchical Expert](https://mlsanity.com/paper/2412.13736/) (2024)
- [Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning](https://mlsanity.com/paper/2502.19655/) (2025)
- [Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL](https://mlsanity.com/paper/2505.17952/) (2025)
- [A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare](https://mlsanity.com/paper/2502.15871/) (2025)
- [MedGPT: Medical Concept Prediction from Clinical Narratives](https://mlsanity.com/paper/2107.03134/) (2021)
- [Automatic Evaluation of Healthcare LLMs Beyond Question-Answering](https://mlsanity.com/paper/2502.06666/) (2025)
- [Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese](https://mlsanity.com/paper/2309.04175/) (2023)
- [Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support](https://mlsanity.com/paper/2502.18274/) (2025)

## Built on by (most cited first, 10 of 10)

- [Fleming-VL: Towards Universal Medical Visual Reasoning with Multimodal LLMs](https://mlsanity.com/paper/2511.00916/) (2025)
- [OralGPT-Omni: A Versatile Dental Multimodal Large Language Model](https://mlsanity.com/paper/2511.22055/) (2025)
- [Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs](https://mlsanity.com/paper/2605.20270/) (2026)
- [Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification](https://mlsanity.com/paper/2605.17308/) (2026)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (2026)
- [Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches](https://mlsanity.com/paper/2606.01145/) (2026)
- [Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models](https://mlsanity.com/paper/2607.02983/) (2026)
- [Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models](https://mlsanity.com/paper/2607.13860/) (2026)
- [AnesTRACE: Benchmarking Intraoperative Anesthesia from Multimodal Perception to Multi-step Decision-Making](https://mlsanity.com/paper/2609.32740/) (2026)
- [MedEvo: Medical Reasoning Evolution through Knowledge Navigation](https://mlsanity.com/paper/2610.17690/) (2026)

## Abstract (arXiv)

While large language models show promise in medical applications, achieving expert-level clinical reasoning remains challenging due to the need for both accurate answers and transparent reasoning processes. To address this challenge, we introduce Fleming-R1, a model designed for verifiable medical reasoning through three complementary innovations. First, our Reasoning-Oriented Data Strategy (RODS) combines curated medical QA datasets with knowledge-graph-guided synthesis to improve coverage of underrepresented diseases, drugs, and multi-hop reasoning chains. Second, we employ Chain-of-Thought (CoT) cold start to distill high-quality reasoning trajectories from teacher models, establishing robust inference priors. Third, we implement a two-stage Reinforcement Learning from Verifiable Rewards (RLVR) framework using Group Relative Policy Optimization, which consolidates core reasoning skills while targeting persistent failure modes through adaptive hard-sample mining. Across diverse medical benchmarks, Fleming-R1 delivers substantial parameter-efficient improvements: the 7B variant surpasses much larger baselines, while the 32B model achieves near-parity with GPT-4o and consistently outperforms strong open-source alternatives. These results demonstrate that structured data design, reasoning-oriented initialization, and verifiable reinforcement learning can advance clinical reasoning beyond simple accuracy optimization. We release Fleming-R1 publicly to promote transparent, reproducible, and auditable progress in medical AI, enabling safer deployment in high-stakes clinical environments.