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OptiRoulette Optimizer: A New Stochastic Meta-Optimizer for up to 5.3x Faster Convergence

arXiv:2603.06613v1 Announce Type: new Abstract: This paper presents OptiRoulette, a stochastic meta-optimizer that selects update rules during training instead of fixing a single optimizer. The method combines warmup optimizer locking, random sampling from an active optimizer pool, compatibility-aware learning-rate scaling during optimizer transitions, and failure-aware pool replacement. OptiRoulette is implemented as a drop-in, torch.optim.Optimizer-compatible component and packaged for pip installation. We report completed 10-seed results on five image-classification suites: CIFAR-100, CIFAR-100-C, SVHN, Tiny ImageNet, and Caltech-256. Against a single-optimizer AdamW baseline, OptiRoulette improves mean test accuracy from 0.6734 to 0.7656 on CIFAR-100 (+9.22 percentage points), 0.2904 to 0.3355 on CIFAR-100-C (+4.52), 0.9667 to 0.9756 on SVHN (+0.89), 0.5669 to 0.6642 on Tiny ImageNet (+9.73), and 0.5946 to 0.6920 on Caltech-256 (+9.74). Its main advantage is convergence reliabilit

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Stamatis Mastromichalakis · · 1 min read · 18 views
#cs.LG #cs.AI #cs.CV #cs.NE

arXiv:2603.06613v1 Announce Type: new Abstract: This paper presents OptiRoulette, a stochastic meta-optimizer that selects update rules during training instead of fixing a single optimizer. The method combines warmup optimizer locking, random sampling from an active optimizer pool, compatibility-aware learning-rate scaling during optimizer transitions, and failure-aware pool replacement. OptiRoulette is implemented as a drop-in, torch.optim.Optimizer-compatible component and packaged for pip installation. We report completed 10-seed results on five image-classification suites: CIFAR-100, CIFAR-100-C, SVHN, Tiny ImageNet, and Caltech-256. Against a single-optimizer AdamW baseline, OptiRoulette improves mean test accuracy from 0.6734 to 0.7656 on CIFAR-100 (+9.22 percentage points), 0.2904 to 0.3355 on CIFAR-100-C (+4.52), 0.9667 to 0.9756 on SVHN (+0.89), 0.5669 to 0.6642 on Tiny ImageNet (+9.73), and 0.5946 to 0.6920 on Caltech-256 (+9.74). Its main advantage is convergence reliability at higher targets: it reaches CIFAR-100/CIFAR-100-C 0.75, SVHN 0.96, Tiny ImageNet 0.65, and Caltech-256 0.62 validation accuracy in 10/10 runs, while the AdamW baseline reaches none of these targets within budget. On shared targets, OptiRoulette also reduces time-to-target (e.g., Caltech-256 at 0.59: 25.7 vs 77.0 epochs). Paired-seed deltas are positive on all datasets; CIFAR-100-C test ROC-AUC is the only metric not statistically significant in the current 10-seed study.

Executive Summary

The article introduces OptiRoulette, a stochastic meta-optimizer that achieves up to 5.3x faster convergence than traditional optimizers. It presents a novel approach to selecting update rules during training, combining warmup optimizer locking, random sampling, and compatibility-aware learning-rate scaling. The results demonstrate significant improvements in mean test accuracy across five image-classification suites, with OptiRoulette outperforming the AdamW baseline in all cases. The method's reliability and efficiency make it a promising tool for deep learning applications.

Key Points

  • ▸ OptiRoulette is a stochastic meta-optimizer that selects update rules during training
  • ▸ It combines warmup optimizer locking, random sampling, and compatibility-aware learning-rate scaling
  • ▸ OptiRoulette achieves up to 5.3x faster convergence than traditional optimizers

Merits

Improved Convergence

OptiRoulette's ability to adaptively select update rules leads to faster convergence and improved test accuracy

Reliability

The method's reliability in reaching high targets makes it a promising tool for deep learning applications

Demerits

Limited Statistical Significance

The current 10-seed study may not be sufficient to establish statistical significance for all metrics, such as CIFAR-100-C test ROC-AUC

Expert Commentary

The introduction of OptiRoulette marks a significant advancement in the field of deep learning optimization. By adaptively selecting update rules during training, OptiRoulette achieves faster convergence and improved test accuracy. The method's reliability and efficiency make it a promising tool for deep learning applications. However, further research is needed to fully establish the statistical significance of the results and to explore the potential applications of OptiRoulette in various fields.

Recommendations

  • ✓ Further research should be conducted to establish the statistical significance of the results
  • ✓ OptiRoulette should be explored for its potential applications in various deep learning tasks and domains

Sources

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