Research reveals that reward model scoring exhibits significant prompt dependency: pairwise evaluation fails to detect this bias.
An AI research paper published on arXiv on October 24, 2026, points out that current mainstream pairwise evaluation methods automatically cancel out the scoring components common to all responses under the same prompt, leaving a critical “prompt-level bias” in reward models unexamined. Using human ratings as a benchmark, the authors conducted an audit of 18 publicly available scorers and found that, on the HelpSteer2 dataset, 6 models exhibited 34%–64% of their rating variance attributable to the prompt itself—whereas human ratings show virtually no such phenomenon.
Why it matters：The study highlights a long-overlooked fundamental flaw in the alignment process of large language models: reward model scores are heavily biased toward the prompt text rather than the actual quality of the response. Its methodology—human‑reference auditing, prompt substitution, and variance decomposition—is reproducible, and its conclusions are grounded in empirical analysis across multiple mainstream open-source scorers, offering direct implications for preference optimization, RLHF, and safety‑assessment practices.