核心内容摘要
3步实现IDM无限试用：彻底解决激活弹窗的高效方案
第001/30页（英文原文）Scaling Laws for Neural Language ModelsJared Kaplan ∗Johns Hopkins University, OpenAIAbstractWe 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