constitutional-ai

Constitutional AI reduces harmful outputs by having models critique and revise their own responses against a set of principles, creating preference comparisons automatically without large volumes of human preference labels.

To apply RLAIF for safety alignment, generate AI feedback through self-critique revisions, train a reward model from those AI judgments, and fine-tune the policy using PPO-style reinforcement learning.

Automated self-critique and revision involves generating a response, having the model identify violations of a constitution, and iteratively producing revised, safer outputs based on that evaluation.

Yes, applying RLAIF and PPO-style reinforcement learning with reward models for safety alignment requires dedicated compute resources for both the fine-tuning and iterative evaluation loops.

Chain-of-thought critiques provide explainable evaluations by generating transparent reasoning about safety trade-offs, allowing you to understand why a response violates or aligns with constitutional principles.

Constitutional AI scales safety alignment better than RLHF by using automated AI self-critique for preference generation rather than relying on large volumes of resource-intensive human preference labels.