RLAIF: Reinforcement Learning from AI Feedback

From https://towardsdatascience.com/rlaif-reinforcement-learning-from-ai-feedback-d7dbdae8f093

The drastic improvement in large language model (LLM) quality is attributed to advancements in the alignment process, 
particularly through finetuning techniques like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). 
RLHF involves training a language model based on human-provided preferences, 
but it requires a large amount of human preference labels, making it expensive and time-consuming.

Recent research has explored automating the collection of human preferences for RLHF using AI,
leading to a new technique known as reinforcement learning from AI feedback (RLAIF).
RLAIF involves training a language model to be helpful and harmless by leveraging AI-provided feedback 
for collecting harmful preference data instead of relying solely on human annotators.

The process involves training a reward model over pairs of model responses, 
where one response is preferred over the other based on human or AI feedback. 
RLAIF has been applied to tasks like text summarization, and the results indicate that 
it can produce comparable improvements to RLHF without depending solely on human annotators.

The key components of RLAIF include:
1. Automating Preference Labels: Using AI-generated feedback, specifically from an off-the-shelf large language model.
2. Preamble and Few-Shot Examples: Including instructions and optional examples to guide the model in generating preference labels.
3. Advanced Prompting Techniques: Such as few-shot prompting, chain of thought prompting,
   and self-consistency to enhance the quality of AI-generated preference labels.
4. Soft Preference Labels: Using log probabilities and softmax to create a "soft" preference distribution for more nuanced feedback.

The approach of automating the RLHF process with AI feedback has shown promising results in terms of scalability, 
efficiency, and alignment quality. It involves training language models that are both helpful and harmless, 
addressing the trade-off between these two objectives. The research suggests that RLAIF is a viable alternative to RLHF,
making the alignment process more accessible and effective for large language models.