Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle

Abstract:Data strategy plays a central role in training large language models (LLMs). It governs the selection, weighting, and scheduling of training data, thereby determining the capabilities a model can acquire from a fixed data budget. We treat high-quality data as having high training-dependent utility, which depends on the target objective, the model, and its training state rather than on the text alone. Research in this area now spans pretraining and post-training, but related ideas use inconsistent terminology and evaluation protocols. This work reviews data strategies across the training lifecycle and formulates them as policies that allocate a training budget over candidate examples. We organise methods by their decision granularity, model dependence, and temporal adaptivity, then compare their computational requirements and empirical support. Fine-grained or state-dependent policies offer more control but incur additional scoring, search, or rollout costs. Evidence for transfer across model scales and comparable end-to-end cost remains limited. A continuously updated list of papers, along with their public implementations, is available at this https URL.

Computation and Language (cs.CL)

arXiv:2610.21681 [cs.CL]

(or arXiv:2610.21681v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.21681 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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