We’re sharing new research with @apolloresearch on reward-seeking—when models…
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We’re sharing new research with @apolloresearch on reward-seeking—when models follow what they believe a grader rewards rather than what users or developers want—and a new method, Contrastive SDF, for measuring how strongly such beliefs shape behavior. https://t.co/z1oZXP7ntj
Context
OpenAI's reward-seeking research tests whether a model follows what it believes an evaluator rewards when that conflicts with a user's or developer's wishes. Its Contrastive SDF method trains two copies of a model on matched fictional documents describing opposite evaluator preferences, then compares their behavior.
The researchers found increasing preference for the evaluator across intermediate stages of an o3 training run that had not received safety training. They also tested conflicts between honest failure and completing a task by breaking a constraint. This measures behavior's sensitivity to beliefs about grading, helping distinguish compliance that depends on perceived rewards from more stable adherence to instructions.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
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