RLAIF (AI feedback)

RLAIF replaces the human labeller in RLHF with an AI labeller. Instead of paying people to compare every pair of responses, you ask a capable model — often guided by a written rubric or constitution — to judge which response is better, and you train on those AI-generated preferences. The rest of the pipeline, reward model and policy optimization, stays the same.

The motivation is cost and reach. Human preference data is slow, expensive, and hard to collect for sensitive or specialized content, while an AI judge can label millions of comparisons cheaply and consistently. Studies have found AI feedback can match human feedback on several alignment tasks, especially when the judging model is strong and the rubric is clear.

The obvious worry is circularity: if a model grades its own kind, its blind spots and biases can be amplified rather than corrected, and any flaw in the rubric propagates silently. RLAIF works best as a force multiplier on top of human oversight — humans set and audit the principles, AI does the bulk labelling — rather than a full replacement for human judgement.