RLAIF
Also called Reinforcement Learning from AI Feedback
Like RLHF, but another AI does the rating instead of humans, to save time and money.
Think of it like
Like grading a mountain of essays with a well-trained teaching assistant instead of the professor — faster, cheaper, and mostly consistent.
Example
Instead of paying labelers to rank thousands of answers, a strong model scores them against a rubric and those scores train the target model.
How it actually works
RLAIF swaps human preference labels for judgments from an AI, often a larger model applying a written set of principles. It scales feedback far beyond what human raters could produce and cuts cost dramatically. The obvious risk is circularity: the student inherits the judge’s biases and blind spots, so a flawed evaluator quietly teaches flawed taste.
For product teams
A way to scale alignment cheaply — but you’ve outsourced your taste to another model, flaws and all.
For engineers
RLHF pipeline with an AI evaluator (often guided by a principles rubric) generating the preference signal.
Related
- RLHF — The human version is RLHF.
- Constitutional AI — Often paired with Constitutional AI.
- Reward model — Trains a reward model.
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