arXiv:2606.06888v1 Announce Type: new Abstract: Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus. As training compute grows faster than the supply of natural language data, pretraining is likely to enter a data-constrained, compute-rich regime where models train for multiple epochs over a finite dataset. We study data-constrained pretraining along two axes, regularization and scaling. For regularization, we study masked-input regularization (MIR), an auxi
The accelerating pace of AI development and compute availability is creating a data scarcity problem for large language models, forcing a re-evaluation of pretraining strategies.
This research directly addresses the looming bottleneck of data scarcity in AI development, indicating a shift in how large models will be trained and optimized, impacting resource allocation and long-term scaling.
The focus moves from simply scaling model size and data in tandem to optimizing training on finite datasets, emphasizing regularization techniques and multi-epoch training.
- · AI research labs
- · Cloud providers with ample compute
- · Companies with proprietary, curated datasets
- · Developers of advanced regularization techniques
- · Companies relying solely on massive, undifferentiated public datasets
- · GPU manufacturers if compute efficiency gains reduce demand growth
AI models will become more sophisticated in learning from limited data, potentially achieving higher efficiency.
Increased competition for high-quality, unique datasets will intensify, driving up their value and potentially leading to new data synthesis methods.
This could democratize AI development by reducing the need for impossibly large datasets, making advanced model training more accessible to entities with strong compute but moderate data.
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Read at arXiv cs.LG