Reinforcement Learning from Human Feedback—a machine learning training method where human evaluators score and rank model responses to train a reward model that fine-tunes model outputs.

The premier technical acronym of the mid-2020s AI boom. Dropped in pitch decks and AI policy meetings to signal elite model alignment and human-guided safety tuning.

• Acronym Flex: Dropping "RLHF" in corporate strategy meetings to sound like a senior OpenAI research scientist.
• Human Labeling Mask: Using "RLHF" to disguise armies of low-paid human clickworkers manually rating chatbot text.
• Kitchen Reality: Like tasting soup, giving it a score out of 10, and calling it "Reinforcement Learning from Human Feedback."

Westworld (2010s) scene where Bernard Lowe sits across from host bodies, asking questions, scoring their responses, and adjusting behavioral attributes based on feedback.

A model training method where human reviewers rate and rank generated answers to help improve response quality.

“Our foundation models undergo extensive RLHF to ensure helpful and safe customer interactions.”

“We used human reviewers to rate and rank generated text summaries so our software model learns to write clearer answers.”