Title: Rethinking Data Curation in LLM Training: Online Reweighting  Offers Better Generalization than Offline Methods
Primary area: foundation or frontier models, including LLMs
Keywords: Data Selection, Large Language Models

Abstract: Data curation is a critical yet underexplored component in large language model (LLM) training. Existing approaches (such as data selection and data mixing) operate in an offline paradigm, decoupled from the training process. This separation introduces extra engineering overhead and makes curated subsets brittle: once the model or task changes, the entire pipeline must be re-run. Moreover, offline methods alter dataset size through hard filtering or resampling, often discarding data diversity, and thus face the generalization issue. We propose to rethink data curation as an online reweighting problem, where sample importance is dynamically adjusted during training via loss weighting rather than static preprocessing. This view preserves data diversity, adapts continuously to evolving model states, and yields a better performance–FLOPs tradeoff. Thus, we introduce ADAPT (Adaptive Data reweighting for Pretraining and FineTuning), a dynamic online framework that reweights training samples with adaptive per-sample learning rates guided by similarity-based quality signals, without changing the number of training samples. ADAPT integrates reweighting directly into the optimization loop with negligible overhead. Experiments on both instruction tuning and large-scale pretraining show that ADAPT consistently outperforms offline selection/mixing and prior online methods, achieving stronger cross-benchmark generalization under equal FLOPs.