Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
Predicting model performance at larger scales enables the design of training strategies and architectures tailored to specific performance targets. Empirical scaling law research identifies functional forms to aid this prediction task. These describe the relationship between loss and compute using a loss-compute frontier defined by learning curves. Due to the empirical nature of this approach, the computational burden is substantial, making strategic resource allocation essential - yet it remains surprisingly underexplored. In this work, we address this shortcoming by exploring the suitability of Successive Halving (SH) and SH combined with parametric and non-parametric surrogate models. ...
| Authors | Viktoria Schram, Markus Hiller, Daniel Beck et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 25 May 2026 |
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