The process: (1) generate multiple responses to a prompt, (2) have a strong AI model (the "judge") compare pairs and indicate which is better, (3) use these AI-generated preferences to train a reward model or apply DPO directly. The judge model can be prompted with specific criteria ("prefer the more helpful, honest, and harmless response") or given a constitution of principles.
Research shows that RLAIF can match RLHF quality for many tasks, especially when the judge model is significantly stronger than the model being trained. The gap is largest for subjective tasks (creative writing quality, cultural sensitivity) where human judgment captures nuances that AI feedback misses. The practical approach: use RLAIF for the bulk of training data and reserve expensive human annotation for edge cases and evaluation.
RLAIF enables self-improvement: a model generates responses, judges them, and trains on its own feedback. This sounds like it could lead to unlimited improvement, but in practice, the gains plateau — a model can't reliably judge responses that are better than its own capability. You can't pull yourself up by your bootstraps. This is why using a stronger judge model than the one being trained is important for meaningful improvement.