While Reinforcement Learning from Human Feedback (RLHF) provides a significant framework for LLM alignment, discussed in the previous chapter, it represents only one part of the available toolkit. This chapter presents several other advanced alignment algorithms that offer alternative mechanisms or address specific challenges encountered with standard RLHF.

You will learn about:

• Constitutional AI: A method that uses AI-generated principles, or a 'constitution', to guide model behavior during fine-tuning, aiming for self-correction based on these rules.
• Reinforcement Learning from AI Feedback (RLAIF): An approach similar to RLHF but substituting AI-generated feedback for human preference labels, potentially scaling the feedback process.
• Direct Preference Optimization (DPO): A technique that directly optimizes the language model policy based on preference data, bypassing the need for an explicit reward model. This often involves a loss function like $L_{DPO}$ derived from a theoretical connection to the RLHF objective.
• Contrastive Methods: Techniques that apply contrastive learning principles, differentiating between desired and undesired outputs to improve alignment.
• Theoretical Strategies: An introduction to concepts such as iterated amplification and debate, which explore alternative paradigms for achieving complex alignment goals.

We will examine the operational details of these methods, compare their relative advantages and disadvantages, and prepare you to apply them through practical exercises, such as implementing the core DPO loss calculation. This provides a broader understanding of the techniques available for guiding LLM development.