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.
翻译:我们研究关于跨热带损失的语言模型性能的经验衡量法。损失等级作为具有模型大小、数据集大小和用于培训的计算量的电法,有些趋势超过7个数量级。其他建筑细节,如网络宽度或深度,在范围上影响最小。简单方程式决定了过度配置模型/数据集大小和培训速度对模型大小的依赖性。这些关系使我们能够确定固定计算预算的最佳分配。较大模型的抽样效率要高得多,因此最优化的计算效率培训涉及就相对较少的数据对非常大模型进行培训,并在趋同之前大量停止。