Verified75% confidenceFactExact time
Scaling Law research has found that model performance exhibits a smooth power-law relationship with the number of model parameters N, dataset size D, and training compute C
3
Sources
75%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
Related Claims
UnverifiedScaling Law research found that model performance (measured by cross-entropy loss) has a predictable power-law relationship with model parameter count, dataset size, and computational cost.80% similarVerifiedThe Scaling Law was first systematically quantified by Kaplan et al. in 2020 in language model research, pointing out a power-law relationship between model performance and parameter count, data volume, and compute.75% similarVerifiedScaling Law was discovered by OpenAI researchers in 2020 and states that model performance improves predictably when model parameter count, training data volume, and compute scale at specific ratios.75% similarVerifiedScaling Law refers to the predictable and regular improvement in model capability as the number of parameters and the volume of training data increase.74% similarUnverifiedThe slope α correlates with the model's predictive performance and may capture structured knowledge learned by the model during training that goes beyond generic extreme-value effects.70% similar
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