Technical requirements

For this chapter, you are expected to possess a solid foundation in machine learning (ML) concepts, particularly in the areas of Transformers and reinforcement learning. An understanding of Transformer-based models, which underpin many of today’s LLMs, is vital. This includes familiarity with concepts such as self-attention mechanisms, positional encoding, and the structure of encoder-decoder architectures.

Knowledge of reinforcement learning principles is also essential, as we will delve into the application of RLHF in the fine-tuning of LMs. Familiarity with concepts such as policy gradients, reward functions, and Q-learning will greatly enhance your comprehension of this content.

Lastly, coding proficiency, specifically in Python, is crucial. This is because many of the concepts will be demonstrated and explored through the lens of programming. Experience with PyTorch or TensorFlow, popular ML libraries, and Hugging Face’s Transformers...