Parameter-efficient continual fine-tuning: A survey

The advent of large-scale pre-trained models has fundamentally transformed the field of artificial intelligence, unlocking new possibilities and achieving unprecedented performance across a wide range of tasks. Yet, these models inherit a fundamental limitation from traditional Machine Learning approaches: their heavy reliance on the i.i.d. assumption, which restricts their capacity to adapt to dynamic, real-world scenarios. We argue that the next major breakthrough in AI lies in developing systems capable of efficient and continual adaptation to evolving environments, where new data and tasks arrive sequentially. This need defines the domain of Continual Learning (CL), a Machine Learning paradigm aimed at developing neural models capable of learning throughout their lifespan without forgetting previous knowledge. In parallel, Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as powerful tools for adapting large models to specific tasks with minimal computational resources. While PEFT techniques can match the performance of full fine-tuning with significantly fewer parameter updates, they remain vulnerable to catastrophic forgetting. This survey bridges the gap between CL and PEFT by focusing on the emerging field of Parameter-Efficient Continual Fine-Tuning (PECFT). We first provide a comprehensive overview of CL strategies and PEFT approaches, then we review recent advances in PECFT. We analyze existing methods, compare evaluation protocols and identify open challenges and promising directions for future research. Our aim is to underscore the synergy between CL and PEFT, offering insights and guidance to researchers seeking to build adaptive, scalable and efficient AI systems.