New survey bridges continual learning and parameter-efficient fine-tuning
v_lomonaco · x · 2026-07-21
A new survey titled “Parameter-Efficient Continual Fine-Tuning: A Survey” bridges two research lines that usually evolve separately: continual learning and parameter-efficient fine-tuning (PEFT).
The paper gives an overview of both fields, reviews recent progress in PEFT for continual settings, and discusses open challenges, evaluation protocols, and future directions for adaptive AI systems that can learn sequentially without forgetting.
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