Amortizing Scaling Law Construction Costs
Quick summary
arXiv:2609.05016v1 Announce Type: cross Abstract: Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameters, token budgets, and parameter counts, which is computationally expensive. Fitting a scaling law, however, only requires the best-loss frontier across compute scales, discarding most of the trained configurations. We propose a framework for efficient scaling law construction that formulates data collection as a Bayesian optimization problem, and introduce metrics for comparing scaling law fitting
Key takeaways
- arXiv:2609.05016v1 Announce Type: cross Abstract: Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameters, token budgets, and parameter counts, which is computationally expensive.
- Fitting a scaling law, however, only requires the best-loss frontier across compute scales, discarding most of the trained configurations.
- We propose a framework for efficient scaling law construction that formulates data collection as a Bayesian optimization problem, and introduce metrics for comparing scaling law fitting
Why it matters
“Amortizing Scaling Law Construction Costs” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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