This course, Reasoning LLMs from Scratch – Full Series, explores the advanced techniques required to build large language models capable of reasoning and decision-making. Starting with an introduction to reasoning LLMs, the series covers chain-of-thought reasoning, verifiers, and beam search for improved model output quality.

The course dives deep into reinforcement learning concepts, including basics, multi-arm bandits, Markov decision processes, value functions, dynamic programming, Monte Carlo methods, and temporal difference learning for both prediction and control. Each lecture explains the theoretical concepts and demonstrates how to implement them in practice to create LLMs that can reason effectively and solve complex tasks.

By following this series, learners will gain a comprehensive understanding of how reasoning can be integrated into LLM architectures, combining advanced reinforcement learning methods with natural language processing techniques. The course equips AI researchers, machine learning engineers, and developers with the knowledge and coding skills needed to enhance the capabilities of large language models for real-world applications.