RLHF Implementation Guide
Comprehensive methodology for building effective RLHF preference data pipelines for LLM alignment.
Understanding RLHF
Reinforcement Learning from Human Feedback (RLHF) has become the dominant technique for aligning large language models. The quality of your preference data directly determines the quality of your aligned model — garbage in, misaligned model out.
Key RLHF Data Types
Preference Pairs: Side-by-side response comparisons with quality rankings across multiple dimensions
Direct Scoring: Absolute quality ratings on defined dimensions (helpfulness, safety, accuracy, harmlessness)
Rejection Sampling: Expert selection of best responses from sampled model candidates
Constitutional Feedback: Principle-based critique and revision of model outputs
Red Team Data: Adversarial prompts and appropriate refusal or response pairs
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