RLAIF (Reinforcement Learning from AI Feedback) is the technique of using AI models (instead of humans) to provide the preference feedback for RLHF — a separate AI model evaluates and compares outputs, providing preference labels at scale without human annotators.

RLAIF Pipeline

• AI Evaluator: A separate (often larger) AI model rates or compares model outputs according to specified criteria.
• Criteria: The AI evaluator is prompted with rubrics for helpfulness, harmlessness, accuracy, etc.
• Scale: AI feedback can label millions of comparisons — far beyond human annotation capacity.
• Self-Improvement: The same model can sometimes evaluate its own outputs (constitutional AI pattern).

Why It Matters

• Cost: AI feedback is orders of magnitude cheaper than human feedback.
• Scale: Enables RLHF-style training at scale that would be infeasible with human annotators alone.
• Quality: RLAIF can achieve comparable quality to RLHF for many tasks — AI judges correlate well with human preferences.

RLAIF is AI teaching AI — using AI-generated preferences instead of human preferences for scalable, cost-effective alignment.