LoRA
Low-Rank Adaptation. Rather than updating every weight, it trains small rank-decomposition matrices injected alongside a frozen base model, which cuts trainable parameters by orders of magnitude and lets one base model serve many task-specific adapters.
Why exams ask this
Asked as a cost and deployment question, not a math question. The exam wants "many small adapters can share one frozen base model in memory," contrasted against full fine-tuning, which produces a whole separate model per task.
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