Enhancing Line Search Methods for Neural Network Training on Large Scales

Core Concepts

Line search methods are enhanced for large-scale neural network training, outperforming traditional approaches.

Abstract

The paper discusses improvements in line search methods for neural network training.
Authors propose enhancements to existing methods and evaluate their effectiveness on larger datasets.
Integration of momentum term from ADAM improves performance and stability.
Evaluation focuses on Transformers and CNNs in NLP and image data domains.
ALSALS algorithm outperforms previous methods and fine-tuned optimizers.
Implementation available as a Python package for PyTorch optimizers.
Background on Armijo line search and modifications for ADAM direction.
Failure cases identified in large-scale transformer training due to divergence in gradient and update directions.
Detailed methods, experimental design, and results for NLP and image tasks.
ALSALS recommended as a hyperparameter-free optimizer for deep neural networks.

Stats

"Our results consistently show that our enhanced Automated Large Scale ADAM Line Search (AL-SALS) algorithm outperforms both the previously introduced SLS and fine-tuned optimizers."
"We found good values for c to be in the range c ∈ [0.3, 0.7]."
"For GPT-2 training, we use the peak learning rate of η = 6 · 10−4 as described in [17] and use a warm-starting period of 2000 steps for all algorithms."

Quotes

"Our enhanced Automated Large Scale ADAM Line Search (AL-SALS) algorithm outperforms both the previously introduced SLS and fine-tuned optimizers."
"We found good values for c to be in the range c ∈ [0.3, 0.7]."
"For GPT-2 training, we use the peak learning rate of η = 6 · 10−4 as described in [17] and use a warm-starting period of 2000 steps for all algorithms."

Key Insights Distilled From

Improving Line Search Methods for Large Scale Neural Network Training

by Philip Kenne... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18519.pdf

Deeper Inquiries

How can the ALSALS algorithm be adapted for other types of neural networks beyond Transformers and CNNs

The ALSALS algorithm can be adapted for other types of neural networks beyond Transformers and CNNs by considering the specific characteristics and requirements of those networks. For instance, when applying ALSALS to recurrent neural networks (RNNs), one would need to account for the sequential nature of the data and the long-range dependencies that RNNs are designed to capture. This adaptation could involve modifying the step size selection process to take into consideration the unique challenges posed by sequential data processing. Additionally, for graph neural networks (GNNs), the adaptation of ALSALS would need to consider the graph structure and the propagation of information through nodes and edges. This could involve incorporating graph-specific loss landscape analysis and step size adjustments tailored to the graph topology.

What potential drawbacks or limitations might arise from relying solely on line search methods for optimization in neural network training

Relying solely on line search methods for optimization in neural network training may present some drawbacks or limitations. One potential limitation is the computational cost associated with line search methods, especially when dealing with large-scale datasets and complex neural network architectures. Line search methods often require multiple forward passes for each update step, which can be computationally expensive and time-consuming. Additionally, line search methods may struggle with noisy or non-convex loss landscapes, leading to suboptimal convergence or getting stuck in local minima. Moreover, the effectiveness of line search methods heavily relies on the choice of hyperparameters, such as the step size adjustment parameter, which may require manual tuning and expertise.

How can the concept of loss landscapes and step size selection be applied to other optimization algorithms in machine learning

The concept of loss landscapes and step size selection can be applied to other optimization algorithms in machine learning to improve convergence and performance. For instance, in gradient descent-based algorithms like SGD, incorporating insights from loss landscapes can help in determining appropriate learning rates and step sizes for faster convergence. By analyzing the curvature of the loss surface, one can adjust the step size dynamically to navigate through steep and flat regions efficiently. This adaptive step size selection can enhance the robustness of optimization algorithms to noisy gradients and complex optimization surfaces. Furthermore, the concept of loss landscapes can be utilized in conjunction with other optimization techniques, such as momentum-based methods or adaptive learning rate algorithms, to fine-tune the optimization process and achieve better generalization and convergence properties.