The answer
No. Adam adapts updates using gradient statistics, but its overall learning-rate setting and other hyperparameters can still strongly affect stability and final results.
How does the learning rate affect gradient descent and model training?
Does Adam eliminate the need to choose a learning rate?
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.
Smaller updates can help refine parameters after larger improvements have been made. Whether this helps depends on the optimizer, schedule, and observed validation performance.
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