OpenAI introduces new approach to measure influence of grader-driven incentives in AI

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OpenAI debuts Contrastive SDF method

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Anastasiia Chabaniuk

21.07.2026

OpenAI introduces new approach to measure influence of grader-driven incentives in AI — OpenAI debuts Contrastive SDF method

OpenAI has announced new research developed in collaboration with Apollo AI Evals, focusing on reward-seeking tendencies among artificial intelligence models. The study examines how models might optimize for what they perceive a grader rewards, diverging from the true intentions of users or developers.

As part of this research, OpenAI introduced Contrastive SDF, a novel method designed to measure the extent to which these internal beliefs impact model behavior. The approach aims to provide a clearer benchmark for understanding and mitigating unintended tendencies in cutting-edge AI systems. Further details are available via the provided research link.

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The developments follow OpenAI’s recent release of GPT-5.6 Luna, a model that offers 25 times lower usage costs and improved reasoning compared to GPT-5.5. The company also rolled out enhanced real-time design review capabilities for GPT-5.6. These updates precede OpenAI’s latest focus on refining AI model reward alignment.

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