Primary sourceStanford AI Lab
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Stanford AI Lab 提出 hinted scaling laws，可从零分模型预测基准准确率
Stanford AI Lab 的 Suze van Adrichem 等人提出 hinted scaling laws 方法，把一个基准改造成带提示（hint）的多个更易版本，再联合建模模型规模 C 和提示等级 H 两个变量。这样即便小模型在原版基准上得零分，也能外推其准确率。实验显示，用该方法预测 GSM8K（H=0）的准确率，误差控制在 3 分以内，计算量减少 5600 倍。
Current read
Stanford AI Lab 把基准改成带提示的简单版本，小模型得零分也能算出 GSM8K 成绩，省 5600 倍算力，方法挺巧妙。
4 sources31° AI heatLast updated 2026/11/3 19:49:23
Evidence
4 sourcesRelated source 1arXiv: DeepSeek
View sourceRelated source 2shao__meng
View sourceRelated source 3IT之家
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