Core Viewpoint - The article discusses the evolution of Parameter-Efficient Fine-Tuning (PEFT) methods, particularly focusing on the challenges and advancements in tensorized PEFT, specifically the ReFTA method, which aims to eliminate the bottleneck of weight reconstruction during training [1][6][25]. Group 1: PEFT Methods Overview - Full Fine-tuning (FF) leads to high memory usage, training time, and storage costs, prompting a shift towards PEFT, which freezes most pre-trained parameters and updates only a few [1]. - The most common PEFT methods utilize low-rank matrix decomposition, such as LoRA, which approximates weight changes with fewer parameters but faces limitations as model size increases [1][6]. - Recent research has begun exploring tensorized PEFT, which stacks similar layers to form a high-order tensor structure, allowing for better modeling of intra-layer and inter-layer dependencies [1][6]. Group 2: ReFTA Method - ReFTA aims to eliminate the extra overhead caused by weight tensor reconstruction by rearranging the computation order, focusing on generating intermediate features before feature fusion [6][18]. - This method transitions the computation paradigm from handling large weight tensors to processing batch-related intermediate features, enhancing efficiency [6][18]. - ReFTA maintains a compact parameter representation while significantly reducing training peak memory usage and overall implementation complexity [18][26]. Group 3: Theoretical and Empirical Support - ReFTA provides a theoretical upper bound on generalization, indicating that reducing tensor rank R decreases complexity at a specific rate, supported by empirical results showing competitive performance with fewer parameters [21][22]. - The method demonstrates robust performance across various models, achieving high accuracy while minimizing the number of trainable parameters [22][23]. - ReFTA's approach to avoiding explicit weight reconstruction not only reduces memory usage but also aligns with theoretical insights on effective parameter control and computational complexity [22][26]. Group 4: Future Directions - The core idea of avoiding explicit tensor weight reconstruction through tensor algebra strategies has broader implications, potentially benefiting model compression and efficient model architecture design [25][26]. - The article suggests that this direction holds significant potential for further exploration and development in the field of machine learning and model optimization [25].