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Apple researchers unveil data mixture scaling laws for LLM pretraining

By PulseAugur Editorial · [1 sources] ·

Apple Machine Learning Research has published findings on optimizing data mixtures for pretraining language models, particularly when dealing with limited target data. Their extensive study, involving over 2,000 training runs, reveals that repeating scarce target data up to 15-20 times is more effective than previously thought, especially when combined with abundant generic data. The research introduces a new scaling law that accounts for the diminishing value of repeated tokens and the regularizing effect of generic data, offering a principled method for configuring data mixtures under constraints. AI

IMPACT Provides a principled method for optimizing data mixtures in LLM pretraining, potentially improving efficiency and performance on specialized tasks.

RANK_REASON The cluster contains a research paper detailing new methods and findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apple researchers unveil data mixture scaling laws for LLM pretraining

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The cluster contains a research paper detailing new methods and findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Scaling Laws for Mixture Pretraining Under Data Constraints

    As language models scale, the amount of data they require grows – yet many target data sources, such as low-resource languages or specialized domains, are inherently limited in size. A common strategy is to mix this scarce but valuable target data with abundant generic data, whic…

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