Researchers have introduced Online Reasoning Calibration (ORCA), a novel framework designed to calibrate the sampling process in large language models (LLMs) at test-time, thereby enhancing their reasoning capabilities. This approach addresses the inefficiencies and exorbitant compute costs associated with state-of-the-art LLMs, which often stem from miscalibration and inadequate sampling techniques1. By leveraging test-time training, ORCA enables LLMs to generalize and conform to various tasks, leading to more accurate and reliable results. The framework's ability to calibrate LLMs in real-time mitigates the need for extensive retraining, reducing computational costs and making it a viable solution for practical applications. This development matters to practitioners because it has the potential to significantly improve the efficiency and effectiveness of LLMs in solving complex tasks, making them more suitable for real-world deployment.