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Scaling Laws for Neural Language Models

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Model/data/compute scaling.

FreeFree tier
Type
Open Source
Company
OpenAI
Visit Website GitHub (40 stars)

About Scaling Laws for Neural Language Models

This research paper, 'Scaling Laws for Neural Language Models,' presents an empirical study of how language model performance, measured by cross-entropy loss, scales with model size, dataset size, and the amount of compute used for training. The authors find that loss follows a power-law relationship across more than seven orders of magnitude, while architectural details like network width or depth have minimal effects. Simple equations describe overfitting as a function of model and data size, as well as training speed versus model size. These findings enable optimal allocation of a fixed compute budget: larger models are significantly more sample-efficient, so compute-efficient training involves training very large models on a relatively modest amount of data and stopping before convergence.

Key Features

Empirical scaling laws for language model cross-entropy loss
Power-law scaling with model size, dataset size, and compute (spanning >7 orders of magnitude)
Minimal effect of architectural details (width, depth) within a wide range
Simple equations for overfitting dependence on model/dataset size and training speed on model size
Optimal compute-efficient training: very large models on modest data, stopping before convergence

Pros & Cons

Pros
  • Provides a unified, data-driven understanding of how language model performance scales with resources
  • Enables practitioners to optimally allocate compute budgets for training
  • Findings span multiple orders of magnitude, offering broad empirical validity
  • Practical guidance for training large models efficiently (e.g., stop early, use large models)
Cons
  • Focused only on cross-entropy loss, not downstream task performance
  • Empirical results may not hold for architectures or training setups outside the studied range
  • No theoretical derivation; purely observational scaling laws
  • Assumes a specific training methodology (e.g., fixed optimization schedule)

Best For

Guiding resource allocation and budget decisions for training large language modelsInforming model design and scaling strategies for compute-constrained settingsUnderstanding overfitting dynamics and sample efficiency in language model training

FAQ

What is the main finding of this paper?
The loss of neural language models scales as a power-law with model size, dataset size, and the compute used for training, with trends spanning more than seven orders of magnitude.
How do architectural choices affect scaling?
Within a wide range, architectural details such as network width and depth have minimal effects on the scaling behavior.
What is the optimal way to train a language model given a fixed compute budget?
The optimal strategy is to train very large models on a relatively modest amount of data and stop significantly before convergence, because larger models are more sample-efficient.