The paper titled "ORFit: One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares," published on arXiv, introduces ORFit, a novel method designed for one-pass learning. This approach uniquely combines orthogonal gradient descent with recursive least-squares to tackle the challenges associated with training large machine learning models in real-world scenarios where data arrives in a streaming fashion. By addressing these challenges, ORFit aims to enhance the efficiency and practicality of model training. The method's effectiveness has been positively noted, suggesting it offers a promising solution for continuous learning environments. This development aligns with ongoing research efforts to improve learning algorithms that can operate effectively under constraints imposed by streaming data. Overall, ORFit represents a significant step toward more scalable and adaptive machine learning methodologies.