What Makes a Dialog Agent Useful?

This post details Reinforcement Learning from Human Feedback (RLHF) as a method to align dialog agent behavior with human preferences. It builds upon previous discussions of Instruction Fine-Tuning (IFT) and Supervised Fine-Tuning (SFT) by detailing the practical application and understanding of RLHF, including the use of preference datasets and reward modeling to guide agent responses towards helpfulness and safety. The post likely provides concrete examples or code snippets demonstrating how to implement RLHF for dialog agents.