reinforcement learning from AI feedback (RLAIF)

Reinforcement learning from AI feedback (RLAIF) is a training technique that aligns a large language model with desired behavior by using another AI model, rather than human annotators, to judge which of two candidate outputs is better.

RLAIF mirrors the pipeline of reinforcement learning from human feedback (RLHF) but swaps the human labelers for an off-the-shelf LLM. The AI labeler ranks pairs of responses, a reward model learns to predict those preferences, and a reinforcement learning algorithm optimizes the model against that reward signal.

The direct RLAIF (d-RLAIF) variant skips the separate reward model and reads rewards straight from the labeler. The full pipeline, including that shortcut, runs from candidate output pairs to an aligned model:

The method was introduced in Anthropic’s Constitutional AI work, where a written list of principles guides the labeler, and later scaled up by Google researchers, who found it can match RLHF on summarization and conversational tasks. RLAIF cuts the cost of human annotation but inherits the labeler’s biases and can reinforce its mistakes.

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