Cautious Optimizers: Improving Training with One Line of Code

Summary

This paper discusses a new approach called Cautious Optimizers, which improves the training of machine learning models with just one line of code, making existing optimizers faster and more stable.

What's the problem?

Many machine learning models use an optimizer called AdamW for training, but researchers have been looking for ways to make optimizers faster and more reliable. Existing methods often require complex changes or new algorithms, which can be difficult to implement and test.

What's the solution?

The authors propose a simple modification to any momentum-based optimizer, like AdamW, which they call the Cautious Optimizer. This change preserves important mathematical properties of the optimizer, ensuring that it can still converge correctly during training. They show that this new approach can speed up training times by up to 1.47 times on popular models like Llama and MAE, without sacrificing performance.

Why it matters?

This research is significant because it provides a straightforward way to enhance the efficiency of existing training methods in machine learning. By making it easier for researchers to implement these improvements, Cautious Optimizers could lead to faster and more effective model training across various applications in AI.