Rank One Update

Rank-one updates are efficient methods for modifying existing models or data structures by making small, targeted changes, primarily focusing on minimizing computational cost while maximizing impact. Current research emphasizes applications in diverse fields, including large language model fine-tuning (using techniques like LoRA and novel skeleton selection methods), high-definition map updates for autonomous driving, and optimizing machine learning algorithms (e.g., CMA-ES). These advancements improve model adaptability, reduce training times, and enhance the efficiency of various applications, particularly in resource-constrained environments.