Verified90% confidenceFactTime unknown
Scaling 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.
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5/31/2026
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VerifiedScaling Law refers to the predictable and regular improvement in model capability as the number of parameters and the volume of training data increase.81% 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.76% similarVerifiedScaling 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 C75% similarVerifiedIn 2020, Kaplan et al. at OpenAI revealed that model performance follows predictable power-law relationships with parameter count, training data volume, and compute budget.72% similarVerifiedThe scaling laws were systematically demonstrated in 2020 by OpenAI researcher Jared Kaplan and others, with the core finding that the cross-entropy loss of language models follows a power-law relationship with model parameter count, training data volume, and compute72% similar
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