RLHF Implementation GuideA methodology for building effective RLHF preference data pipelines for LLM alignment.

Understanding RLHF

Reinforcement learning from human feedback is now the dominant technique for aligning large language models. The quality of your preference data decides the quality of the aligned model: garbage in, misaligned model out.

Key RLHF data types

• Preference pairs: Side-by-side response comparisons ranked across multiple dimensions.
• Direct scoring: Absolute quality ratings on helpfulness, safety, accuracy and harmlessness.
• Rejection sampling: Expert selection of the best response from sampled model candidates.
• Constitutional feedback: Principle-based critique and revision of model outputs.
• Red team data: Adversarial prompts paired with appropriate refusals or responses.