@misc{aihist_scaling-laws-2020,
  title        = {{OpenAI 提出 Scaling Laws，规模化成为大模型路线}}
  author       = {{AI 纪年}}
  date         = {2020-01-23}
  year         = {2020}
  month        = jan
  howpublished = {\url{https://aihist.net/e/scaling-laws-2020.html}}
  url          = {https://aihist.net/e/scaling-laws-2020.html}
  urldate      = {2026-11-03}
  language     = {zh-CN}
  keywords     = {机器学习, 大模型, 缩放定律, 论文}
  abstract     = {Kaplan 等人发现语言模型损失随参数量、数据量与计算量呈幂律下降，并给出可外推的拟合公式。它把“把模型做大”从经验判断变成可预算的工程路线，直接支撑 GPT-3 及此后的大模型竞赛；2022 年 Chinchilla 又修正了参数与数据的配比。}
  note         = {AI 纪年静态引用页（EB/OL），构建于 2026-11-03}
}