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Life sciences · Preprint
arXiv · September 4, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes a Bayesian optimization framework to reduce the computational cost of deriving scaling laws for large language models by selecting informative configurations rather than training a full grid. The authors claim computational savings of 10–100× while maintaining accuracy comparable to exhaustive grid search, but the work is unrefereed and lacks independent validation or head-to-head comparison with existing scaling law methods.
Preprint. Intervention: Bayesian optimization framework with progressive compute budget expansion and surrogate-fantasized evaluations for scaling law construction.. Compared with: Full dense grid evaluation (exhaustive hyperparameter and token budget sweep)..
Progressive expansion of compute budget during acquisition improves recovery efficiency of scaling law frontiers. Surrogate-fantasized evaluations augment observed configurations and allow accurate scaling law fitting without training every configuration. Computational savings of up to 10–100× reported relative to full dense grid evaluation.
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This is an unrefereed methodological paper proposing a computational framework for scaling law construction; it presents algorithmic innovation with simulated efficiency gains but lacks peer review and clinical or translational validation.
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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 methods under constrained compute budgets. We find that progressively expanding the compute budget during acquisition, mirroring the compute-ordered evaluation of configurations in practice, substantially improves recovery efficiency. Augmenting the observed configurations with surrogate-fantasized evaluations then recovers the broader experimental grid, allowing accurate scaling law fitting without training every configuration. Together, these can closely match scaling law fits over a full dense grid at computational savings of up to $10\text{--}100\times$.
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