Rater State Bias in RLHF Preference Data: An Audit Framework
We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation. Under sustained stressful or distressing conditions, raters' preferences may shift over time, so that preference data encode rater state alongside judgments about response quality. We argue that, if present, such shifts would differ from ordinary disagreement or random label noise. They would be state dependent, could be shared across annotators under similar conditions, and would not necessarily cancel during aggregation, reward modeling, and policy optimization. We p...
| Authors | Elena Kopteva, Vitaliy Hlynianyi-Zhuk |
|---|---|
| Primary category | cs.AI |
| Published | 14 April 2026 |
| Last updated at source | 28 September 2026 |
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