ResearchResearch paperLarge Language Models1 source · Oct 15, 2026

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

Data strategy plays a central role in training large language models (LLMs).

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.
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.
Evidence for transfer across model scales and comparable end-to-end cost remains limited.

[1]Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 11:08 AM
Data strategy plays a central role in training large language models (LLMs).
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.

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