TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints
arXiv:2605.29183v1 Announce Type: new Abstract: As machine learning(ML) systems evolve to continual adaptation, each re-training cycle uses compute, annotation, and energy. We introduce TIMEGATE, a policy layer managing adaptation by budgeting time, labeling, training, and evaluation. TIMEGATE emits a metric-availability signal M for partial vs. full-evaluation decisions. We validate: (i) labeling outperforms training by 2.3x on Adult tabular; (ii) it transfers to LLaMA-3.1-8B + QLoRA on SST-2 (accuracy 0.80 to 0.96; M =1 in 35/36 runs); (iii) M is informative, 28-cell sensitivity shows M drop
The increasing computational and energy demands of continual ML adaptation necessitate new strategies for resource management and efficiency as ML systems become more ubiquitous.
This research offers a method to sustainably scale ML systems by optimizing resource allocation, directly impacting operational costs and environmental footprint for AI development and deployment.
Machine learning adaptation cycles can now be managed with a policy layer that budgets crucial resources, shifting from unconstrained retraining to more strategic, cost-aware continual learning.
- · AI developers
- · Cloud providers
- · Organizations deploying continual ML
- · Inefficient ML adaptation methodologies
- · High-energy-consumption data centers
Reduced operational costs and energy consumption for machine learning systems.
Accelerated development and wider adoption of continually adapting AI in resource-constrained environments.
Potential for new business models around optimized AI operations and carbon-neutral ML services.
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Read at arXiv cs.LG