arXiv cs.LG论文
层次化数据选择：基于流形覆盖与稀疏特征覆盖的LLM后训练方法
该论文提出MASS方法，用于大语言模型后训练中的数据选择。现有方法在原始嵌入空间度量多样性，易混淆语义方向、监督差异和噪声。MASS通过粗到细的层次覆盖：先用密集自编码器学习低维主流形坐标进行粗粒度语义分组，再用TopK稀疏自编码器进行组内质量感知的稀疏特征覆盖。在Vision Flan和LLaVA-CoT上，MASS在多种预算下优于强基线，且仅用少量数据即可匹配或超越全量训练。
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Abstract: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.