Rater State Bias in RLHF Preference Data: An Audit Framework

• URL: http://arxiv.org/abs/2607.16195v2
• Date: Wed, 22 Jul 2026 02:34:16 GMT
• Title: Rater State Bias in RLHF Preference Data: An Audit Framework
• Abstract summary: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF)<n>Under sustained stressful or distressing conditions, raters' preferences may shift over time, so that preference data encode rater state alongside judgments about response quality.<n>We propose rater state shift as a plausible and testable source of structured bias in RLHF preference data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 propose rater state shift as a plausible and testable source of structured bias in RLHF preference data. This paper develops a hypothesis and an audit framework for studying this source of bias. We define rater state shift, rater state confound, and correlated rater state bias. We also propose survival level emotional authenticity as a candidate output signature, defined by lexical, pragmatic, discourse, and safety features whose reliability and validity remain to be demonstrated. We analyze the conditions under which correlated rater state bias would not be averaged out during aggregation and could enter the learned reward signal. We state five predictions that distinguish this mechanism from generic engagement optimization, together with effect size thresholds for an initial audit, and note which require proprietary data. Finally, we present an audit protocol and pilot study plan that can be applied to publicly available instruction tuned models. We do not infer the training history of any specific deployed model.

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