arXiv:2610.33440v1 Announce Type: new Abstract: Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate perturbation-based data selection using two signals derived from Gaussian perturbations of the final-block representation: prediction-change frequency and non-flipping margin contraction. After a shared entropy prefilter, Representation-Sensitivity Prioritization (RSP) combines the signals using automatic root-mean-square scaling, while RSP-Drop ranks candidates by contraction alone. Both selectors use only questions and answer options during selection. We evaluate them with MedGemma-4B on five medical QA datasets at retained-set budgets of 64, 128, and 256 examples. At the primary budget of 128,
arXiv:2610.33440v1 发布类型：新 摘要：从现有问答库中适配医学语言模型时，选择一个紧凑的训练子集是至关重要的一步。我们研究了基于扰动的数据选择方法，该方法利用由最终层表示的高斯扰动所衍生的两种信号：预测变化频率和非翻转边距收缩。在经过一个共享熵预过滤器后，表示敏感性优先化（RSP）通过自动均方根缩放将这两种信号进行整合；而RSP-Drop则仅依据边距收缩对候选样本进行排序。这两种选择器在筛选过程中仅使用问题和答案选项。我们在保留集规模分别为64、128和256个样本的情况下，以MedGemma-4B为基准，在五个医学问答数据集上对其进行了评估。在主要预算规模128个样本时，
