<ul><li>Pretraining large language models effectively requires strategic data selection, blending and ordering.</li><li>A two-phase pretraining approach outperforms random data ordering and natural distribution of tokens.</li><li>The two-phase approach improves average accuracies by 3.4% and 17%.</li><li>Guidance is provided on crafting optimal data blends based on data source quality and the number of epochs.</li></ul>

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining

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