arXiv:2606.16246v1 Announce Type: cross Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, compute-abundant regime that demands productive multi-epoch training on fixed corpora. Standard autoregressive (AR) pretraining overfits severely in this setting, reaching its optimum early and then continuously deteriorating. We investigate data augmentation as a regularizer to mitigate this overfitting and enable productive training for hundreds of epochs on the same
The accelerating pace of AI development and the growing realization of data scarcity are forcing immediate solutions to optimize training efficiency.
This research addresses a critical constraint in large language model development, potentially extending the utility of existing datasets and impacting the future compute and data strategies of major AI labs.
The focus for large language model pretraining shifts towards more efficient use of finite, high-quality data through techniques like augmentation, rather than purely scaling data size.
- · AI labs with limited proprietary datasets
- · Data augmentation technology providers
- · Compute infrastructure providers (as more epochs are run)
- · Companies solely relying on data acquisition as a competitive advantage
- · AI models that cannot effectively utilize data augmentation
Language models trained with these techniques will achieve better performance and generalization on fixed datasets, extending their useful lives.
The economic barrier to entry for training competitive large language models may decrease, as data quantity becomes less of a sole determinant.
This could lead to a 'data-efficient AI' paradigm, where innovation focuses on algorithmic efficiency and data synthesis over raw data collection, affecting the entire AI supply chain.
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Read at arXiv cs.CL