Meta新论文提出Skaling law：耦合容量与数据的新缩放定律
Meta新论文搞了个Skaling law，比Chinchilla和Kaplan那套准1.5到3倍，而且小规模跑就能算准，预算算力能省不少。
Meta新论文提出Skaling law，通过单一交互指数耦合模型容量与训练数据。该定律将平均绝对百分比误差在插值和外推中降低1.5倍至3倍。在数据稀缺和重度过训练区域，Chinchilla与Kaplan传统定律的预测会漂移，Skaling law的修正幅度最大。配合稀疏网格，只需约十分之一计算量即可外推完整训练网格。论文预印本编号为arXiv:2608.07222。
Impressive new paper from Meta. (bookmark it) Scaling laws assume model size and training data act on loss independently. This work introduces Skaling law, which couples capacity and data through a single interaction exponent. The extra term cuts mean absolute percentage error by 1.5x to 3x across both interpolation and extrapolation. The largest corrections land in the data-scarce and heavy-overtraining regimes where the standard Chinchilla and Kaplan forms drift. Paired with a sparse grid restricted to low-compute runs, it extrapolates the full grid using roughly 10x less compute than a uniform sweep. Why does it matter? Deployment now happens well past compute optimal. A law that stays accurate there, and that can be fit from small runs, changes how a pretraining budget gets planned. Paper: arxiv.org/abs/2608.07222 Track more trending AI papers in our academy: academy.dair.ai 💬 12 🔄 24 ❤️ 197 👀 11277 📊 65 ⚡
