Abstract The increasing intricacy and scale of deep learning (DL) models have heightened the necessity for effective weight and parameter optimization algorithms that sustain superior performance while reducing computational resource consumption. This paper thoroughly analyzes the evolution of parameter optimization strategies, ranging from initial methodologies to modern advancements, elucidating their principles and applications in natural language processing (NLP) and machine learning (ML). We pay special emphasis to parameter-efficient fine-tuning (PEFT) approaches, such as low-rank adaptation (LoRA) and its extensions, which facilitate the adaptation of large language models (LLMs) on resource-limited devices. These methods address challenges such as high computational demands, energy consumption, and deployment restrictions, thereby promoting more accessible and environmentally sustainable artificial intelligence (AI) solutions. By integrating methodological understandings with recent advancements, this survey underscores the essential role of parameter optimization in enabling scalable deep learning systems. It serves as an essential guide for researchers seeking to apply these techniques across diverse domains, emphasizing their impact on achieving efficient and robust model performance.
Najafi et al. (Fri,) studied this question.