constitutional-ai

AI safety alignment via RLAIF trains models for harmlessness by applying a two-phase approach: supervised learning with AI self-critique and revision, followed by Reinforcement Learning from AI Feedback to minimize harmful outputs.

RLAIF (Reinforcement Learning from AI Feedback) improves safety alignment by using an AI model's own feedback and a defined set of ethical principles to iteratively refine responses, reducing harmful and toxic outputs at scale without human labeling.

Yes, you can implement Constitutional AI using Hugging Face transformers, torch, and the trl library. This Skill provides the scripts and references needed to integrate these dependencies for RLAIF training workflows.

The best way to fine-tune a large language model to avoid offensive content is applying a constitutional AI methodology with RLAIF, allowing the model to self-critique and revise its responses against predefined ethical principles.

The two-phase Constitutional AI process works by first using supervised learning where the model critiques and revises its own outputs based on a constitution, followed by a reinforcement learning phase using AI feedback to enforce safety alignment.

No, RLAIF training does not require a manually labeled safety dataset. It enables scalable safety alignment by using AI-generated feedback and a predefined constitution to iteratively refine the model, bypassing costly human labeling.