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[2503.09411] Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
Summary
This article explores the advantages of learning rate annealing in stochastic optimization, demonstrating its robustness against initial parameter misspecification and its potential to reduce computational overhead in hyperparameter tuning.
Why It Matters
As machine learning models grow in complexity, optimizing hyperparameters like the learning rate becomes increasingly challenging. This research highlights a method to improve tuning robustness, which can lead to more efficient training processes and better model performance, making it highly relevant for practitioners in the field.
Key Takeaways
- Learning rate annealing can enhance robustness in stochastic optimization.
- The convergence rate with annealed schedules improves compared to fixed stepsize methods.
- This approach can significantly reduce the computational costs associated with hyperparameter tuning.
Computer Science > Machine Learning arXiv:2503.09411 (cs) [Submitted on 12 Mar 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization Authors:Amit Attia, Tomer Koren View a PDF of the paper titled Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization, by Amit Attia and Tomer Koren View PDF HTML (experimental) Abstract:The learning rate in stochastic gradient methods is a critical hyperparameter that is notoriously costly to tune via standard grid search, especially for training modern large-scale models with billions of parameters. We identify a theoretical advantage of learning rate annealing schemes that decay the learning rate to zero at a polynomial rate, such as the widely-used cosine schedule, by demonstrating their increased robustness to initial parameter misspecification due to a coarse grid search. We present an analysis in a stochastic convex optimization setup demonstrating that the convergence rate of stochastic gradient descent with annealed schedules depends sublinearly on the multiplicative misspecification factor $\rho$ (i.e., the grid resolution), achieving a rate of $O(\rho^{1/(2p+1)}/\sqrt{T})$ where $p$ is the degree of polynomial decay and $T$ is the number of steps. This is in contrast to the $O(\rho/\sqrt{T})$ rate obtained under the inverse-square-root and fixed stepsize schedules, which depend linearly on $\rho$. Experiments confirm the inc...
