Verified75% confidenceFactExact time
The 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 compute
5
Sources
75%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
The Truth About "GPT-5.6": Debunking Fake Rumors and Third-Party Top-Up Scams
bilibili小小阿白6667/9/2026
Related Claims
UnverifiedThe neural network scaling laws proposed by Kaplan et al. in 2020 indicate that model performance has a power-law relationship with data volume, parameter count, and compute, where the model's cross-entropy loss L and parameter count N satisfy L ∝ N^(-α).77% similarUnverifiedScaling 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.76% 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% similarUnverifiedNeural network scaling law research from institutions such as OpenAI has demonstrated power-law relationships between model loss and parameter count, data volume, and compute.74% 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.74% similar
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