Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs
Medical LVLMs are prone to factual inconsistencies and poor visual grounding. Existing alignment methods have three key limitations in the medical domain: sequence-level rewards treat clinically critical tokens equally, reliance on static SFT references causes off-policy shift, and alignment lacks visual grounding constraints. The proposed method uses a bidirectional token-wise KL regularizer and a visual-contrastive grounding objective, forming a fine-grained on-policy alignment framework that constructs preference pairs by minimally editing model outputs. Experiments validate its effectiveness.
[2606.12590] Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs
[Submitted on 10 Jun 2026]
Title:Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs
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Abstract:Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks, yet they remain prone to factual inconsistencies, poor visual grounding, and misalignment with clinically meaningful feedback. Existing post-training alignment approaches, including Direct Preference Optimization (DPO) and its variants, face three critical limitations in the medical domain: (1) sequence-level reward signals treat clinically critical tokens identically to generic filler text; (2) reliance on static supervised fine-tuning references as preferred responses introduces an off-policy distribution shift, steering optimization toward stylistic artifacts over clinical correctness; and (3) alignment objectives lack explicit visual grounding constraints, leaving models insensitive to subtle yet diagnostically decisive pathological features. Our method leverages a bidirectional token-wise KL regularizer alongside a visual-contrastive grounding objective that pairs clean and lesion-corrupted images to penalize responses generated without adequate visual evidence. Together, these components form a fine-grained, on-policy alignment framework that constructs preference pairs by minimally editing model-generated outputs, correcting only clinically erroneous spans while preserving the original linguistic style. Extensive experiments across medical imaging tasks and clinical text generation benchmarks validate the effectiveness of our approach.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12590 [cs.CV]
(or arXiv:2606.12590v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12590
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shayan Mohammadizadehsamakosh [view email] [v1] Wed, 10 Jun 2026 18:35:36 UTC (5,015 KB)
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