This academic paper explores methods to improve the efficiency and accuracy of Large Language Models (LLMs) during the final step of generating responses, known as test-time scaling. The authors propose Self-Calibration, a technique to teach LLMs to reliably estimate their own confidence in an answer with a single pass. By incorporating these calibrated confidence scores, they develop efficient test-time scaling strategies, such as stopping repeated sampling early when a confident answer is found or weighting sampled answers by confidence. Experimental results demonstrate that these confidence-based approaches enhance performance and computational efficiency compared to traditional methods that sample a fixed number of responses. The paper highlights the importance of reliable confidence estimation for optimizing LLM inference.