Successive Halving guided by parametric and non-parametric surrogates finds better loss-compute frontiers for scaling laws while using up to 98.7% less compute than exhaustive uniform allocation.
In this step, each model is trained with budget Cr, either for the first time ( r= 0 ) or as a continuation of its learning curve
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
Successive Halving guided by parametric and non-parametric surrogates finds better loss-compute frontiers for scaling laws while using up to 98.7% less compute than exhaustive uniform allocation.