Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection

## 📄 2604.22753v1 **作者**: Sijie Li, Shanda Li, Haowei Lin, Weiwei Sun, Ameet Talwalkar **分类**: cs.LG **发表**: 2026-04-24 ### 摘要 Scaling laws are used to plan multi-million-dollar training runs, but fitting those laws can itself cost millions. In modern large-scale workflows, assembling a sufficiently informative set of pilot experiments is already a major budget-allocation problem rather than a routine preprocessing step. We formulate scaling-law fitting as budget-aware sequential experimental design: given a finite pool of runnable experiments with heterogeneous costs, choose which runs to execute so as to maximize extrapolation accuracy in a high-cost target region. We then propose an uncertainty-aware method for sequentially allocating experimental budget toward the runs most useful for target-region extrapolation. Across a diverse benchmark of scaling-law tasks, our method consistently outperforms classical design-based baselines, and often approaches the performance of fitting on the full experimental set while using only about 10% of the total training budget. Our code is available at https://github.com/PlanarG/active-sl. 🔗 arXiv 论文页面 --- 训大模型的Scaling Law是出了名的烧钱，这篇文章直接告诉你怎么省着花——用主动实验选择把预算削减到原来的十分之一，效果还差不多，这思路挺实在的。搞大模型训练的团队应该挺需要这个的。

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