用分层覆盖方法从大数据中选优质子集，提升大模型微调效率。
Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
- 先用稠密自编码器捕捉语义主曲面，粗粒度分组数据
- 再在每组内用稀疏自编码器选关键特征样本，提升质量感知
- 仅用少量数据就达到全量训练效果，适合资源受限场景
随着监督微调数据规模持续增长，从海量候选数据中选取高价值子集对降低训练成本、提升模型性能至关重要。现有方法通常直接在原始嵌入空间衡量多样性，导致几何度量混淆了主导语义方向、细粒度监督差异和局部噪声。本文提出一种分层覆盖的数据选择框架MASS，通过稠密自编码器学习低维主曲面坐标实现粗粒度语义分组，再在每组内使用TopK稀疏自编码器进行质量感知的稀疏特征覆盖。在Vision Flan和LLaVA-CoT上的实验表明，MASS在多种预算下均优于强基线方法，在多个设置中仅用少量数据即达到甚至超过全量数据训练效果。
原文摘要 · Abstract (English)
As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Existing methods often measure diversity directly in the original embedding space, where geometric metrics entangle dominant semantic directions, fine-grained supervision differences, and local noise. We address this limitation by formulating data selection as a coarse-to-fine hierarchical coverage problem and propose MASS. MASS learns low-dimensional principal manifold coordinates with a dense autoencoder for coarse semantic grouping, and then performs quality-aware sparse feature coverage within each group using a TopK sparse autoencoder. Experiments on Vision Flan and LLaVA-CoT show that MASS consistently outperforms strong data selection baselines across multiple budgets, and in several settings matches or surpasses full data training with only a small subset of data.
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