AI RESEARCH
Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios
arXiv CS.CL
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ArXi:2512.00920v5 Announce Type: replace Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods focus solely on preference perception accuracies in given specific scenarios, obscuring the critical vulnerabilities of RMs in real-world scenarios. We identify the true challenge lies in assessing a novel dimension: Suitability, defined as conditional reliability under specific real-world perturbations. To this end, we