2 papers
cs.LG2026
Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
Viktoria Schram, Markus Hiller, Daniel Beck +1
Predicting model performance at larger scales enables the design of training strategies and architectures tailored to specific performance targets. Empirical scaling law research i…
cs.LG2025
Zero-Shot Performance Prediction for Probabilistic Scaling Laws
Viktoria Schram, Markus Hiller, Daniel Beck +1
The prediction of learning curves for Natural Language Processing (NLP) models enables informed decision-making to meet specific performance objectives, while reducing computationa…