HSCR uses visual token dropout and logit contrast to construct self-generated dispreferred answers, then trains a medical VLM with explicit and implicit preference losses, improving zero-shot Rad-VQA, SLAKE, and PathVQA scores.
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HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models
HSCR uses visual token dropout and logit contrast to construct self-generated dispreferred answers, then trains a medical VLM with explicit and implicit preference losses, improving zero-shot Rad-VQA, SLAKE, and PathVQA scores.