Med-OPD: Evidence-Aware Distillation Improves Medical Vision-Language Model Reasoning
Researchers introduce Med-OPD, a post-training framework that combines on-policy distillation with medical evidence-aware supervision for medical vision-language models (Med-VLMs). The approach uses a Medical Evidence Advantage (MEA) signal to focus training on diagnosis-critical tokens and evidence-dependent reasoning. Experiments on OmniMedVQA subsets show that Med-OPD outperforms standard supervised fine-tuning and on-policy distillation methods across multiple medical imaging tasks.
Why it matters: This work offers a novel method to improve the reliability of medical vision-language models by encouraging them to base clinical reasoning on visual evidence rather than language priors.
Full story at: arXiv Computer Vision ↗