constitutional-ai

Constitutional AI improves LLM safety alignment by using self-critique and revision against a rule-based constitution, generating AI preference feedback instead of relying solely on human labels to produce safer responses.

To train a reward model with RLAIF, generate AI preference pairs by critiquing initial responses against constitutional principles, then use these comparisons to train the reward model for PPO-style optimization.

Yes, self-critique and revision support supervised fine-tuning by generating an initial answer, critiquing it against a constitution of principles, and rewriting it into a safer and more helpful response without human labels.

PPO-style optimization works with AI-generated preference feedback by applying reinforcement learning from AI feedback to optimize model responses based on a reward model trained through constitutional principles.

Use RLAIF instead of human-labeled reinforcement learning when reducing toxic, evasive, or poorly justified outputs in chatbots and assistants requires scalable AI-generated preference feedback over costly human labels.

Self-critique for reducing toxic outputs requires a well-defined rule-based constitution and depends on AI-generated preference comparisons, meaning the quality of safety alignment is bounded by the constitutional principles provided.