OPENMEDREASON: Scientific Reasoning Supervision for Medical Vision–Language Models
High-stakes clinical use of large vision–language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers. We introduce OPENMEDREASON, a large-scale, open multimodal medical reasoning corpus comprising approximately 450K image–question–answer instances whose reasoning traces are primarily derived from curated biomedical, human-authored scientific articles. OPENMEDREASON provides high-fidelity supervision beyond synthetic chains of thought, covering diverse medical domain vision modalities such as radiological scans, microscopic images, visible light photographs, charts, and others. We complement it with OPENMEDREASON-Bench, a held-out benchmark that allows fine-grained evaluation of LVLMs along three complementary axes of capability, including perception, medical knowledge, and rationale, enabling diagnostic evaluation beyond final-answer accuracy. OPENMEDREASON is a rich training resource that exhibits its effectiveness in both supervised fine-tuning (SFT) and reinforcement-based alignment. Training with OPENMEDREASON yields a 20% average improvement in VQA accuracy over the base model and achieves performance within 4.2% of the strongest comparablescale medical LVLMs. Fine-grained performance analysis confirms that the gains are not concentrated in any single axis: OPENMEDREASON improves perception, medical knowledge, and rationale jointly, and its reasoning traces are preferred over those of the base model in 86.1% of pairwise comparisons. We release the code and dataset at huggingface.co/datasets/neginb/OpenMedReason
