RLAIF
Reinforcement learning from AI feedback is a training or alignment technique that uses AI-generated evaluations or preferences as feedback to optimize a model or agent.
Introduction to Generative AI, Second Edition
Reinforcement learning from AI feedback is an alignment technique that uses AI-generated evaluations or preferences instead of, or alongside, human feedback to optimize a model.
- How it is used
- Deploys AI-generated evaluations to align language models and support safety post-training at greater scale.
- How deep it goes
- Explains the feedback-to-reward pipeline and major trade-offs, but stops short of implementation details.
- Examples
- Uses AI preference labeling, mathematical and coding verification, safety responses, and DeepSeek R-1 as examples.
- What you can do after
- Evaluate/understand how AI feedback can scale alignment while preserving risks around validation, safety definitions, and accountability.
Read how Introduction to Generative AI, Second Edition explains RLAIF →
Reinforcement Learning from Human Feedback
Reinforcement learning from AI feedback is a reinforcement-learning alignment technique that uses AI-generated evaluative feedback as its reward or preference signal.
- How it is used
- Deploys AI-generated preferences and evaluations to accelerate post-training, reward modeling, and large-scale model assessment.
- How deep it goes
- Defines the pipeline role and trade-offs, but defers detailed reward-model objectives and implementation mechanics.
- Examples
- Uses Constitutional AI, pairwise preferences, LLM-as-a-judge evaluation, and rubric-based rewards as concrete applications.
- What you can do after
- Apply it via an RLHF pipeline to scale preference collection, reward training, evaluation, or rubric-based reinforcement learning.
Read how Reinforcement Learning from Human Feedback explains RLAIF →
Related concepts
- reinforcement learning with human feedbackalternative to · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
- Constitutional AIused by · Constitutional AI is an alignment approach that guides model behavior by evaluating or shaping it according to an explicit set of principles.
- AI feedbackuses · AI feedback is evaluative or supervisory information generated by an artificial-intelligence system about another system’s outputs, actions, or behavior.
- evaluatoruses · An evaluator is a model or component that assesses outputs against specified criteria and produces judgments, scores, comparisons, or feedback.
- post-trainingkind of · Post-training is the additional training and adaptation of a pretrained model to improve its capabilities, behavior, alignment, or usefulness for particular tasks.
- rubricuses · A rubric is a structured set of explicit criteria and guidance for evaluating and assigning judgments or scores to an output, artifact, or behavior.
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