LoRA Meets Foundation Models: Unlocking Efficient Specialization for Scalable AI

The proliferation of foundation models—massive pre-trained architectures with billions of parameters—has redefined the landscape of deep learning. While these models achieve remarkable performance across a wide range of tasks, their fine-tuning poses significant computational and storage challenges, particularly in low-resource or multi-task scenarios. Low-Rank Adaptation (LoRA) has emerged as a principled and practical solution to this bottleneck, enabling efficient specialization of frozen base models via lightweight, trainable rank-constrained updates. This survey provides a comprehensive overview of LoRA in the context of foundation models. We begin by formalizing the core intuition behind low-rank updates, analyzing their expressivity, regularization properties, and connections to classical matrix theory. We then explore the expanding ecosystem of LoRA variants and extensions, including quantized, sparse, and task-conditioned forms. Comparisons with alternative parameter-efficient fine-tuning (PEFT) methods such as adapters, prompt tuning, and BitFit highlight LoRA's distinctive strengths in mergeability, modularity, and scalability. Practical applications are discussed across NLP, vision, and multimodal domains, with attention to open-source ecosystems and real-world deployments. Finally, we outline key open challenges—from automatic rank selection and robustness to cross-modal generalization—and chart promising directions for future research. This work positions LoRA as a foundational primitive for scalable, modular, and democratized adaptation in the age of foundation models.