Want to explore the emerging class of reasoning-enhanced large language models (LLMs), otherwise known as “reasoning models?” In this course, learn how these models differ from conventional LLMs and why they are particularly well-suited for complex tasks like coding challenges, puzzles, and other areas that require more advanced problem-solving. Through real-world case studies, such as DeepSeek-R1, the course breaks down three main methods for building and improving reasoning capabilities in LLMs. Along the way, get hands-on experience with inference-time scaling, reinforcement learning, and distillation. You’ll also learn about the trade-offs between qualitative performance, efficiency, and development cost when using reasoning models versus conventional models. By the end of this course, you’ll be prepared to build and use reasoning models on your next project.
This course is integrated with GitHub Codespaces, an instant cloud development environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace.
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