AI Research
Improving Data-Constrained Scaling with Hyperparameter Transfer and Ensembling
Research on scaling language models when high-quality training data is limited
As growing compute budgets outpace the growth of high-quality training data, large language models must be trained with both compute and data constraints in mind.
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ArXiv CS.LG
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ArXiv CS.LG
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Generated insight by OnAirToday AI
As compute budgets grow faster than high-quality training datasets, data availability can limit how effectively models use additional compute. Hyperparameter transfer and ensembling are the approaches named for improving scaling under that constraint.
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