Verified90% confidenceFactExact time
The 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.
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90%
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Long-term
Relevance
7/22/2026
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Related Claims
VerifiedThe scaling laws established by Kaplan et al. in 2020 indicate that model performance follows a power-law relationship with parameter count, data volume, and compute.82% 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.76% 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 compute76% similarUnverified扩展假设由OpenAI研究员Jared Kaplan等人于2020年的扩展定律论文中量化阐述，核心是模型性能与参数量、数据量、计算量存在幂律关系75% 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% similar
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