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[2001.08361] Scaling Laws for Neural Language Models

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Abstract:We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

[2001.08361] Scaling Laws for Neural Language Models Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2001.08361 (cs) [Submitted on 23 Jan 2020] Title: Scaling Laws for Neural Language Models Authors: Jared Kaplan , Sam McCandlish , Tom Henighan , Tom B. Brown , Benjamin Chess , Rewon Child , Scott Gray , Alec Radford , Jeffrey Wu , Dario Amodei View a PDF of the paper titled Scaling Laws for Neural Language Models, by Jared Kaplan and 9 other authors View PDF

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