Difficulty-Aligned Trajectory Matching (DATM)

Difficulty-Aligned Trajectory Matching (DATM) 数据集由新加坡国立大学的研究团队创建，旨在通过难度对齐的轨迹匹配技术，合成一个小型但性能与原始大型数据集相当的高质量合成数据集。该数据集通过匹配早期和晚期训练轨迹，有效解决了传统数据蒸馏方法在合成样本数量极小时有效性的局限。DATM数据集的应用领域包括持续学习、隐私保护和神经架构搜索等，旨在解决模型训练中数据量与性能之间的平衡问题。

The Difficulty-Aligned Trajectory Matching (DATM) dataset was developed by a research team from the National University of Singapore. Its core objective is to synthesize a high-quality small-scale synthetic dataset whose performance is comparable to that of the original large-scale dataset using difficulty-aligned trajectory matching techniques. By matching both early and late training trajectories, this dataset effectively resolves the performance limitation of traditional data distillation methods when the quantity of synthetic samples is extremely limited. The DATM dataset applies to research domains including continual learning, privacy preservation, and neural architecture search, aiming to address the trade-off between data volume and model performance during model training.