In this module, you will explore advanced techniques for fine-tuning large language models (LLMs) using reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO), and direct preference optimization (DPO). You’ll begin by describing how LLMs function as probabilistic distributions and how these can be transformed into policies to generate responses based on input text. You’ll examine the relationship between policies and language models as a function of parameters, such as omega, and how rewards can be calculated using human feedback. This includes training response samples, evaluating agent performance, and defining scoring functions for tasks like sentiment analysis using PPO. You’ll also be able to explain PPO configuration, learning rates, and the PPO trainer’s role in optimizing chatbot responses using Hugging Face tools. The module further introduces DPO, a more direct and efficient way to align models with human preferences. While complex topics like PPO and reinforcement learning are introduced, you are not expected to understand them in depth for this course. The hands-on labs in this module will allow you to practice applying RLHF and DPO. To support your learning, a cheat sheet and glossary are included for quick reference.