Staged Promotion Protocol Reduces Experimental Costs in Micro-Pretraining Configuration Selection
Researchers developed a staged-promotion protocol that uses progressively longer training runs (from 2 minutes to 12 hours) to identify promising neural network configurations while controlling costs. The study found that early-stage rankings can be unstable across different hardware, but a reference configuration remained top-ranked at longer budgets across multiple hardware platforms and random seeds. The work demonstrates how careful experimental design can reduce GPU hours spent on configuration search from 432 to 169 hours while maintaining rigorous validation standards.
A new arXiv preprint describes a systematic approach to reducing computational costs in neural network pretraining experiments through staged evaluation. Starting with twelve pre-screened configurations, researchers evaluated candidates at five increasing budget levels (2, 5, 10, 60, and 12 hours) on two different hardware platforms (Windows A100 and Linux L40S), with frozen promotion rules established before expensive continuations. The study found that rankings at shorter budgets (5 and 10 minutes) were sensitive to hardware choice, but a reference configuration based on Staged Factorial Screening consistently ranked first across all longer evaluation stages and hardware-seed combinations. The full protocol used 169.2 GPU-hours including screening, compared to 432 GPU-hours if all 10-minute candidates had been continued. The authors explicitly note this represents a bounded cost-allocation finding rather than a claim of global optimality or superiority over other hyperparameter optimization methods.
What's missing
The paper does not discuss how this staged-promotion approach compares empirically to established adaptive hyperparameter optimization methods (e.g., Bayesian optimization, population-based training, or bandit-based approaches) on the same configurations and budgets, though the authors acknowledge such comparison as an open question.
What different sources said
- arXiv cs.AICenter
Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining
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