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Empirical relationships showing model performance predictably improves with increases in data, parameters, and compute for large language models.
DESCRIPTION
Kaplan scaling laws describe power-law relationships between loss and training resources for large language models. They indicate predictable gains from scaling dataset size, parameter count, and compute. These laws guide training budgets, dataset curation, and model sizing. Later work, such as Chinchilla, refined compute-optimal trade-offs between data and parameters. Practitioners use these laws to forecast performance and avoid under-training or over-parameterization. They inform decisions on token budgets, batch sizes, and training duration in production-scale LLMs.
ALTERNATIVE DEFINITIONS
arXiv (Kaplan et al., 2020)
We study empirical scaling laws for language models, showing that performance improves predictably as we increase model size, data, and compute.
Establishes power-law scaling and predictable performance trends for LLMs.
OpenAI
The amount of compute used in the largest AI training runs has been increasing exponentially, enabling advances in model capability that follow scaling trends.
Connects scaling laws to exponential growth in training compute.
DeepMind
We show that for a fixed compute budget, optimal model performance is achieved by training smaller models for longer on more data.
Refines scaling trade-offs by emphasizing data over parameters for compute-optimal training.
RELATED TERMS
Neural Scaling Law Transformer Model Training Compute Parameter Count Dataset Size Power-Law Scaling Pretraining Tokens Loss Scaling
COMPETING TERMS
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Chief AI Officer
EXTERNAL SOURCES
- Wikipedia - Neural scaling laws
- arXiv - Original scaling laws paper
- DeepMind - Chinchilla compute-optimal training
- Epoch AI - Survey of neural scaling laws
TOP QUESTIONS
Why are Kaplan Scaling Laws important for LLM planning?
They quantify performance improvements from scaling compute, parameters, and data. Teams use them to forecast outcomes and allocate budgets effectively.
Category: Relevancy
What did Kaplan Scaling Laws originally show?
They revealed power-law loss curves indicating predictable gains from larger models, more training tokens, and increased compute.
Category: Origins
How do Kaplan Scaling Laws compare to Chinchilla Scaling Laws?
Kaplan emphasizes general scaling; Chinchilla optimizes for fixed compute by favoring more data over larger parameter counts.
Category: Alternatives
What common mistakes are made when applying Kaplan Scaling Laws?
Underestimating data quality and overfitting token composition can mislead predictions. Ignoring optimization and regularization weakens results.
Category: Mistakes
How should teams use Kaplan Scaling Laws in practice?
Model sizing and token budgets should match compute constraints. Validate scaling predictions with pilot runs and monitoring.
Category: BestPractices
What risks come from misinterpreting Kaplan Scaling Laws?
Overspending on parameters without sufficient data increases cost and underperforms. Unrealistic extrapolations cause wasted compute.
Category: Risks
Are Kaplan Scaling Laws limited to language models?
They were shown for language models, but similar trends appear in other modalities. Domain-specific adjustments are still necessary.
Category: Scope
What is currently a hot topic around Kaplan Scaling Laws?
Compute-optimal training strategies, token mixture design, and efficient fine-tuning to sustain scaling trends are active areas.
Category: Topic
Where can I learn more about Kaplan Scaling Laws?
Read the original arXiv paper, DeepMind’s compute-optimal study, and surveys from Epoch AI or academic course notes.
Category: Learning