ARES presents a method for adaptive red-teaming and end-to-end repair of policy-reward systems in reinforcement learning. The paper addresses safety and alignment challenges in RL by proposing techniques for adversarial testing and system improvement.
Safety
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
ARES combines adversarial red-teaming with end-to-end repair to automatically identify and fix alignment vulnerabilities in reinforcement learning reward systems.
Wednesday, April 22, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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safety