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Learning from experience
Lightweight adapters
Training small separate modules — a compromise between frozen weights and full retraining.
Parameter-efficient tuning updates a small part of the system and is therefore cheaper than full retraining. It supports controlled specialisation, but does not by itself solve continual learning: sequential updates still have to be tested for loss of earlier skills.
Sources
Microsoft Research · LoRAOpen primary source