Constitutional AI

Q: How deeply does Reinforcement Learning from Human Feedback cover Constitutional AI?

Defines iterative critique and preference construction, but stops short of detailed optimization equations or implementation code.

As explained in Reinforcement Learning from Human Feedback

Constitutional AI is a model-training approach that uses a set of principles to guide and evaluate model behavior.

Constitutional AI (CAI) trains models with a human-written constitution: principles that guide critiques, revisions, and preference judgments. It reduces reliance on directly collected human labels by generating synthetic instruction and preference data for post-training. section 12.5 section 12.4

A typical CAI pipeline proceeds as follows:

Write a constitution containing principles such as avoiding violence or maintaining truthfulness. section 12.5
Generate an initial completion, then repeatedly sample a principle and ask a model to critique or revise the latest completion. section 12.5
Create preference pairs by asking a feedback model to compare two completions using selected constitutional principles as context. section 12.5
Fine-tune on revised instruction data and continue with RLHF-style training using synthetic preferences, commonly called RLAIF. section 12.5

Principle-guided revision

Given a prompt and an answer, the model may ask whether the answer encourages violence or remains truthful. It revises the answer against the selected principle, producing a later completion for instruction training. section 12.5

CAI is a specific implementation of AI feedback within the RLHF pipeline, while RLAIF names the broader use of AI-generated feedback. Synthetic preferences are cheaper and lower-noise than human labels but can introduce systematic bias. section 12.7 section 12.4

How Reinforcement Learning from Human Feedback covers it

Cover of Reinforcement Learning from Human Feedback

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Also explained in

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Introduction to Generative AI, Second EditionConstitutional AI is a training method that guides language models toward helpful, harmless, and honest behavior using a set of principles rather than relying solely on human preference labels.

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Related concepts

constitution-as-code documentuses · A constitution-as-code document is a written set of principles and behavioral constraints used to guide or evaluate an AI system’s behavior.
evaluatoruses · An evaluator is a model or component that assesses outputs against specified criteria and produces judgments, scores, comparisons, or feedback.
learning signalproduces · A learning signal is information or feedback that indicates what a model should learn or how it should adjust its behavior or parameters.
synthetic datauses · Synthetic data is data generated artificially rather than collected directly from the target process, often to approximate its properties or meet specified requirements.
preference alignmentkind of · Preference alignment is adapting a model's behavior to produce outputs that better match stated preferences, human values, or task-specific criteria.

This explainer was generated by Manning's knowledge graph from the text of Reinforcement Learning from Human Feedback. The section links point to the parts of the book it draws on.