EPISODE · Apr 21, 2026 · 12 MIN
AI轻松学-02-浅析大模型 Scaling Law
from AI轻松学
多位研究人员在 Transformer 语言模型上系统地实证研究了损失随模型规模、数据量与训练计算量的标度律，发现交叉熵损失在这三类尺度因子上均呈明确的幂律下降，且该规律跨越数个量级且对网络形状（深度/宽度/头数）影响很弱。给出若干具体幂律关系式（如 L(N), L(D), L(C_min) 及联合形式 L(N,D)、L(N,S)），并证明过拟合、临停步数与临界批量大小等指标也服从可预测的函数形式，从而导出在固定计算预算下最佳的模型/批量/步数/数据分配策略.在小宇宙查看该单集文稿
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