核心概念
The author explores the scaling laws in large language models, emphasizing the importance of predicting loss trajectories accurately and optimizing model configurations. By deriving precise formulas, they aim to shift theoretical understanding to practical implementation for pre-training large language models.
要約
The content delves into the significance of scaling laws in optimizing large language models by predicting loss trajectories accurately. It discusses key factors such as model size, training steps, batch size, and hyperparameters that influence the performance of language models. The experiments validate the efficacy of scaling laws in predicting loss trajectories for different datasets and model sizes.
The study highlights how scaling laws can aid in determining optimal configurations without extensive tuning on very large models. It also addresses challenges such as determining batch sizes, model sizes, computational budgets, data mix ratios, and context lengths efficiently using scaling laws. The goal is to provide a principled methodology for training large language models effectively.
統計
Nc and αN are constant scalars estimated at 1.5 × 10^14 and 0.076 respectively.
Sc and αS are constant scalars estimated at 2.6 × 10^3 and 0.67 respectively.
B∗ is a constant term estimated at 1.7 × 10^8.
Estimated values for parameters on C4 dataset: αN = 0.0615, αS = 0.672, αB = 0.139, Nc = 4.85 × 10^17, Sc = 1.54 × 10^3, B∗ = 2.15 × 10^11.
引用
"Scaling laws play a fundamental role in optimizing various aspects of model pre-training."
"Some subsequent works cast doubt on the general applicability of scaling laws."
"The critical batch size strikes an optimal time/computation balance based solely on the loss value."