arXiv cs.AI· Hongyu Bai, Jiaqi Zeng, Chunzheng Zhu·· 14 小时前AI 评分29
MedCBR：在能力边界上用熵自适应评分标准对齐医疗视觉语言模型
Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding
AI 导读
研究提出 MedCBR 两阶段对齐框架，将边界感知偏好优化与评分标准接地的强化学习结合，在六个医疗基准上平均准确率达 61.2%，在困难的鉴别诊断任务上提升最大。第一阶段用难度加权边界偏好优化（DBPO）聚焦模型能力前沿的反事实边界偏好，第二阶段用熵门控接地策略优化（EGPO）按预测熵激活临床评分标准。消融显示两阶段组合带来超加性增益，超过各自单独改进之和。
正文 · 原文
Abstract:Medical diagnostic reasoning requires verifiable links between visual evidence and clinical knowledge, yet current medical vision language models are aligned with objectives that reward the wrong behavior. Uniform supervised fine tuning lets easy cases dominate updates while clinically critical boundary cases saturate prematurely. Sparse rewards based only on final-answer correctness cannot assess anatomical grounding or clinical logic, inviting reward hacking through answer shortcuts. We propose MedCBR, a two-stage alignment framework coupling boundary-aware preference optimization with rubric-grounded reinforcement learning. First, we construct counterfactual boundary preferences at the model's capability frontier using empirical pass rates and preference pairs that share reasoning prefixes but diverge at decisive diagnostic branches. Difficulty Weighted Boundary Preference Optimization (DBPO) concentrates learning on this calibrated boundary region. Second, we distill reusable clinical rubrics from boundary trajectories and activate them according to prediction entropy. Entropy Gated Grounded Policy Optimization (EGPO) rewards answer correctness, anatomical grounding, and reasoning fidelity while suppressing rubric noise on reliable samples. A reference policy anchored to the boundary-aligned model couples population-level boundary priors with instance-level uncertainty, making diagnostic reasoning more accurate and clinically grounded. Experiments on six medical benchmarks show consistent improvements over strong general, specialized, and reinforcement learning baselines, reaching 61.2% average accuracy with the largest gains on challenging differential diagnosis tasks. Ablations show super-additive gains: combining the two stages exceeds the sum of their individual improvements.
| Comments: | Accepted at ACM Multimedia Asia 2026 (MMAsia 2026). 10 pages, 4 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39835 [cs.CV] |
| (or arXiv:2610.39835v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39835 arXiv-issued DOI via DataCite |
Submission history
From: Chunzheng Zhu [view email]
[v1]
Thu, 29 Oct 2026 11:15:12 UTC (2,078 KB)
来源：arXiv cs.AI · arxiv.org