Reinforcement Learning from AI Feedback (RLAIF)

Human review is expensive. A model can evaluate far more outputs than a human panel, which makes AI feedback attractive for large training runs.

RLAIF uses AI-generated critiques, rankings, or scores as feedback. The judge receives criteria and evaluates candidate outputs. A hybrid system can send uncertain or high-impact cases to people.

Scale changes the economics of oversight and amplifies systematic errors. A judge that misses one failure pattern can approve thousands of similar outputs. A judge can also share training data, style preferences, or blind spots with the policy it evaluates.

Useful controls include judge ensembles, hidden human audit sets, disagreement routing, domain-specific rubrics, and monitoring for reward inflation.