This notebook explains how to make smaller AI models work better by giving them more time to "think" about a problem. This is important because training really big AI models is very expensive. The authors used math problems as an example and found that smaller models could actually outperform larger models if given enough time to solve the problem. They used different techniques to improve the models' performance, like having the model generate multiple possible answers and then picking the best one. They also looked at how to make sure the model doesn't just focus on one solution path but explores different possibilities. By improving these techniques, the authors believe we can make AI more accessible and efficient for a wider range of applications, from chatbots to scientific research.