Rethinking Representativeness and Diversity in Dynamic Data Selection
Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: representativeness and diversity. Instead of local geometric centrality, we define representativeness as coverage of dataset-level common or high-frequency feature factors. Instead of within-subset dispersion, we define diversity at the process level, requiring the selection trajectory to gradually include complementary rare factors over training. Based on this view, we propose a dynamic selection framework with three components. First, we score representativeness in a plug-in feature space to prioritize samples cover...
| Authors | Yuzhe Zhou, Zhenglin Hua, Haiyun Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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