RLAIF works like RLHF, except the feedback that trains the model comes from another AI model instead of a human reviewer. An AI evaluator rates or ranks a model's responses against a set of guidelines, and that feedback is used to adjust the model's behavior, without a person manually reviewing every single response.

‍

‍Why it matters

Human review doesn't scale easily, ranking millions of model responses by hand takes an enormous amount of time and people. RLAIF became a way to keep the same basic feedback loop as RLHF while removing most of the manual review bottleneck, since an AI model can evaluate responses far faster than a team of people can. It doesn't remove humans from the process entirely, it usually just shifts where their time goes, from reviewing every response to writing and refining the guidelines the AI evaluator follows.

‍

‍How it's done in practice

Teams typically write out a clear set of principles or guidelines for the AI evaluator to judge by, similar to the guidelines a human reviewer would use, then have that evaluator model rank or score outputs from the model being trained. Those scores get used the same way human rankings would in RLHF, to train a reward model that shapes the final model's behavior. The tradeoff is that the AI evaluator can only enforce standards as well as the guidelines it's given, so unclear or incomplete guidelines cause the same problems here as they would with human reviewers.

‍

‍How it's different from RLHF

The core difference is who's doing the judging, a person in RLHF, an AI model in RLAIF, but the underlying training approach is largely the same. Anthropic's Constitutional AI research, which trains a model to critique and revise its own responses against a written set of principles, is one of the better known examples of this approach in practice. Many teams end up combining both, using RLHF where human judgment really matters and RLAIF to scale up feedback on lower stakes responses.