The answer
Smaller updates can help refine parameters after larger improvements have been made. Whether this helps depends on the optimizer, schedule, and observed validation performance.
How does the learning rate affect gradient descent and model training?
Why reduce the learning rate near the end of training?
More FAQs in How does the learning rate affect gradient descent and model training?
No. It controls update size rather than hardware throughput. A larger learning rate may reach a useful solution sooner, or it may destabilize training entirely.
No. Adam adapts updates using gradient statistics, but its overall learning-rate setting and other hyperparameters can still strongly affect stability and final results.
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