We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performance. Unlike static methods, \mode adapts selection criteria to training phases: emphasizing class balance early, diversity during representation learning, and uncertainty at convergence. We show that MODE achieves (1-1/e)-approximation with O(n \log n) complexity and demonstrates competitive accuracy while providing interpretable insights into data utility evolution. Experiments show \mode reduces memory requirements

翻译：本文提出MODE（多目标自适应数据效率）框架，该框架能够根据核心集选择策略对模型性能的动态贡献进行自适应组合。与静态方法不同，MODE能够根据训练阶段调整选择标准：在训练初期强调类别平衡，在表征学习阶段关注数据多样性，在收敛阶段侧重不确定性度量。我们证明MODE在O(n log n)复杂度下可实现(1-1/e)近似比，在保持竞争力的准确率同时，为数据效用演化提供可解释的洞察。实验表明MODE能显著降低内存需求。