RLHF
Reinforcement Learning from Human Feedback is a crucial alignment technique that uses human preference data to optimize a language model's behavior and tone.
Think of It Like This
Like an etiquette coach watching a brilliant but wildly inappropriate scholar interact with people, heavily rewarding them when they speak politely and helpfully.
RLHF bridges the gap between raw text completion and helpful assistance. It involves collecting human rankings of model outputs, training a Reward Model on those preferences, and then using PPO to adjust the LLM's weights to maximize that reward. It is the secret sauce behind the commercial success of modern chatbots like ChatGPT.