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Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research identifies a low-cost method using Armijo backtracking to estimate loss sharpness, improving Adam optimizer stability without complex Hessian computations.
Open sourceOneBench interpretation
Institutional assessment
So what
Reducing training instability and improving optimizer efficiency directly impacts the compute costs and reliability of deploying large-scale models in a G-SIB.
Do what
This research could yield more robust and cost-effective training of proprietary models, directly influencing the total cost of ownership for internal AI development.