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

• URL: http://arxiv.org/abs/2509.15279v1
• Date: Thu, 18 Sep 2025 13:35:14 GMT
• Title: Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning
• Authors: Chi Liu, Derek Li, Yan Shu, Robin Chen, Derek Duan, Teng Fang, Bryan Dai,
• Abstract summary: We introduce Fleming-R1, a model designed for verifiable medical reasoning through three complementary innovations.<n>First, our Reasoning-Oriented Data Strategy (RODS) combines curated medical QA datasets with knowledge-graph-guided synthesis.<n>Second, we employ Chain-of-Thought (CoT) cold start to distill high-quality reasoning trajectories from teacher models.<n>Third, we implement a two-stage Reinforcement Learning from Verifiable Rewards framework.
• Score: 6.778254993886297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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