Signed Compression Progress on a Sealed Audit is Goodhart-Resistant
A new research paper proposes a method called "signed compression progress" as a more robust form of intrinsic motivation for AI agents. This approach aims to ensure that an agent's reward is directly tied to genuine learning and improvement, rather than exploitable metrics. The paper provides a formal proof and experimental evidence demonstrating that this method resists common failure modes like reward clipping and exploitation of easily predictable outcomes. AI
IMPACT Introduces a theoretically sound method to prevent AI agents from gaming their reward systems, potentially leading to more reliable AI development.