RLHF Data Quality: What Separates Good Feedback from Great Feedback

Reinforcement Learning from Human Feedback (RLHF) has become the dominant technique for aligning large language models with human preferences. But the quality of the resulting model is entirely dependent on the quality of the human feedback used to train it. Poor feedback produces misaligned models — and misaligned models are expensive to fix.

The Three Dimensions of RLHF Quality

Common Failure Modes

The most common RLHF failure mode is annotator drift — where individual annotators gradually shift their interpretation of the rating criteria over time. This is especially problematic in long-running projects where the same annotators work on thousands of examples. Regular calibration sessions, inter-annotator agreement monitoring, and clear escalation paths for ambiguous cases are essential mitigations.

"An RLHF dataset with 90% annotator agreement is not a dataset with 10% noise — it is a dataset with a systematic disagreement that will be baked into your model's preferences."

Building a High-Quality RLHF Pipeline

Effective RLHF pipelines require careful annotator selection (domain expertise matters), structured onboarding with calibration examples, ongoing quality monitoring, and clear guidelines that cover edge cases. The investment in annotator quality pays dividends in model alignment — and in reduced post-deployment safety incidents.