Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive which architectural choices, optimization techniques, and deployment constraints should guide practical method selection. This overview revisits LoRA through the lens of signal processing (SP), bridging modern adapter designs with classical low-rank modeling tools and inverse problems, as well as highlighting how SP principles can inform principled advances of fine-tuning approaches. Rather than providing a comprehensive enumeration and empirical comparisons of LoRA variants, emphasis is placed on the technical mechanisms underpinning these approaches to justify their effectiveness. These advances are categorized into three complementary axes: architectural design, efficient optimization, and pertinent applications. The first axis builds on singular value decomposition (SVD)-based factorization, rank-augmentation constructions, and cross-layer tensorization, while the second axis deals with initialization, alternating solvers, gauge-invariant optimization, and parameterization-aware methods. Beyond fine-tuning, emerging applications of LoRA are accounted across the entire lifecycle of large models, ranging from pre- and post-training to serving/deployment. Finally, open research directions are outlined at the confluence of SP and deep learning to catalyze a bidirectional frontier: classical SP tools provide a principled vocabulary for designing principled PEFT methods, while the unique challenges facing modern deep learning, especially the overwhelming scale and prohibitive overhead, also offer new research lines benefiting the SP community in return.

翻译：低秩适配（LoRA）已成为基础模型参数高效微调（PEFT）的事实标准，能够在极低计算与存储开销下实现对十亿参数网络的适配。尽管其实证成功及变体方法迅速涌现，但哪些架构选择、优化技术与部署约束应指导实际方法选择仍不明确。本综述从信号处理（SP）视角重新审视LoRA，将现代适配器设计与经典低秩建模工具及逆问题相联结，并强调SP原理如何为微调方法的原理性进展提供依据。本文未对LoRA变体进行全面枚举与实证比较，而是聚焦支撑这些方法有效性的技术机制。这些进展被归纳为三个互补维度：架构设计、高效优化及相关应用。第一个维度基于奇异值分解（SVD）因式分解、秩增强构造与跨层张量化；第二个维度涉及初始化、交替求解器、规范不变优化及参数化感知方法。除微调外，本文还涵盖LoRA在大模型全生命周期中的新兴应用，涵盖预训练、后训练及服务部署环节。最后，在SP与深度学习的交汇处勾勒出开放研究方向，以催化双向前沿：经典SP工具为设计原理性PEFT方法提供规范术语，而现代深度学习面临的独特挑战（特别是超大规模与高昂开销）也反过来为SP领域提供新的研究途径。