Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
7/25/2026
First Seen
Sources
The Rise of General AI: Breakthroughs in Mathematical Reasoning and Competitive Programming
redditr/singularity7/11/2026
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.84% 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% similarUnverified2020年Kaplan等人提出原始Scaling Law，倾向于在固定算力下优先扩大模型参数量75% similarUnverifiedThe 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% similarUnverifiedThe Scaling Law paper by Kaplan et al. in 2020 revealed that, under a fixed compute budget, there is an optimal ratio between model parameter count and the number of training tokens (the Chinchilla optimal).72% similar
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