arXiv:2605.28309v1 Announce Type: new Abstract: In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but how to determine when sufficient evidence has been collected without incurring unnecessary computational cost. We study a learning-based extension of a recent empirical online heuristic that adaptively estimates the required number of runs using outlier handling and skewness-based symmetry checks. Using annotated outcomes from 132{,}000 Nevergrad runs on COCO (24 problems in 20 dimensions, 10 inst
The increasing scale and complexity of AI model training and evaluation necessitate more efficient and reliable methods for benchmarking and resource allocation.
Improving the efficiency of stochastic optimization benchmarking directly reduces computational waste and accelerates AI research and development, which is critical for competitive advantage.
The development of learning-based techniques to adaptively estimate the required number of runs in stochastic optimization introduces a new paradigm for more cost-effective and reliable AI model evaluation.
- · AI researchers
- · Cloud computing providers (through optimized resource use)
- · Organizations developing large-scale AI models
- · Inefficient benchmarking methodologies
- · Organizations with limited compute budgets (if they don't adopt similar efficien
Adaptive number-of-runs estimation leads to faster and more accurate comparisons between different stochastic optimization algorithms.
Reduced computational costs and time for AI model development could accelerate breakthroughs in various AI applications.
More efficient AI development could further entrench the dominance of large-scale AI labs and make catching up harder for smaller players, while also democratizing access to some extent by lowering the cost of experimentation for all.
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