Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
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A Lagrangian-relaxation plus imitation-learning pipeline adaptively allocates test-time compute to LLMs, outperforming uniform baselines by up to 12.8% relative accuracy on MATH while staying within a fixed average budget.
OrthoBO introduces an orthogonal acquisition estimator subtracting an optimally weighted score-function control variate to reduce Monte Carlo variance, preserve the acquisition target, and improve ranking stability in Bayesian hyperparameter optimization.
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On the Invariance and Generality of Neural Scaling Laws
Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
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Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
A Lagrangian-relaxation plus imitation-learning pipeline adaptively allocates test-time compute to LLMs, outperforming uniform baselines by up to 12.8% relative accuracy on MATH while staying within a fixed average budget.
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ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
OrthoBO introduces an orthogonal acquisition estimator subtracting an optimally weighted score-function control variate to reduce Monte Carlo variance, preserve the acquisition target, and improve ranking stability in Bayesian hyperparameter optimization.