What Does This Mean?
Reinforcement Learning from Human Feedback — a training technique where human ratings of model outputs are used to fine-tune an AI model's behavior, central to how modern chatbots like ChatGPT are made helpful and safe.
REAL-WORLD EXAMPLE
"That refusal isn't a hard rule, it's just what RLHF trained the model to say."
LORE & ORIGIN
Builds on earlier reinforcement-learning-from-feedback work such as TAMER, and was formalized and popularized in modern ML research by Christiano et al. in 2017; it became core AI-training vocabulary as it scaled through the GPT-2, GPT-3, and ChatGPT-era training pipelines.
EQUIVALENT CONCEPTS IN OTHER LANGUAGES
MENTIONS OVER TIME
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