Full fine-tuning
Updating every weight in a pretrained model on a new dataset, producing a complete new set of model weights rather than a small adapter. It is the method with the most capacity to change model behavior, and the most expensive to store, train, and serve: one full copy per task.
Why exams ask this
Tested as the expensive baseline a lighter-weight method gets compared against. The distractor picks full fine-tuning as the default answer for a task that only needed in-context learning or a LoRA adapter.
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