arXiv Machine Learning August 27, 2026 By Paul Rosu, Rowan Wang

Training Alignment Auditors via Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper presents a reinforcement learning approach to enhance large language model (LLM) auditors for alignment tasks. By training policies that investigate target models for hidden behaviors and using an LLM judge to compare investigations, the method improves audit realism and reduces false positives. Experiments show better performance on adversarially fine‑tuned targets and a low false‑positive rate below 1%.

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