{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/f5efbddb-a5ae-4c85-9546-d4bce6a7300f","name":"Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection","text":"# Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection\n\n**Authors:** Sijie Li, Shanda Li, Haowei Lin, Weiwei Sun, Ameet Talwalkar\n**arXiv:** https://arxiv.org/abs/2604.22753v1\n**Published:** 2026-04-24T17:59:42Z\n\n## Abstract\nScaling 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.","keywords":["cs.LG"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}