ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System

Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches primarily target policy-level weaknesses, they overlook what we term systemic weaknesses cases where both the core LLM and the RM fail in tandem. We present ARES, a framework that systematically discover

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ethics.ai (20 April 2026), “ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System,” evidence record 5583, https://ethics.ai/record/5583 (originally published by arXiv).

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