Why are small updates often useful?
Gradients describe local behavior around current parameter values. Very large steps can move beyond the region where that information predicts a useful direction.
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More FAQs in How does adjusting parameters improve an AI model’s predictions?
No. Parameters are shared across examples, so an update can improve some predictions while worsening others. Training aims to improve an aggregate objective.
Yes. Some adaptation methods train selected parameters or add small trainable components. Their usefulness depends on the task and the evaluation results.
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