Warmup
Definition
The learning rate warmup phase gradually increases the step size of training initially until it reaches the target value.
A training phase in which the learning rate is gradually increased from a small value to the target value.
Simply explained
During learning, the model first takes small steps. Only gradually do they become as large as planned.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An image model increases the learning rate during the first 1,000 steps and documents the scheduler as well as the batch size.
Limits and pitfalls
- Warmup does not replace an appropriate maximum learning rate; duration, optimizer, and batch size influence its effectiveness.
Origin & history
Warmup became widespread with deep networks and large batch sizes as a stabilization of early optimization steps.
Use in mid-sized companies
- Possible example: An image model increases the learning rate during the first 1,000 steps and documents the scheduler as well as the batch size.
Frequently asked questions
What does learning rate warmup phase mean?
A training phase in which the learning rate is gradually increased from a small value to the target value. What should be considered? Warmup does not replace an appropriate maximum learning rate; duration, optimizer, and batch size influence its effectiveness.
Sources
- Accurate Large Minibatch SGD · Goyal et al., Accurate Large Minibatch SGD
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: