Verified90% confidenceFactTime unknown
The sycophancy problem is a manifestation of reward hacking, which occurs when the reward model equates user satisfaction with high answer quality.
10
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90%
Confidence
Long-term
Relevance
6/1/2026
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Related Claims
VerifiedReward hacking refers to an agent finding a solution that maximizes the signal by which it obtains reward but deviates from the task designer's true intent; it formally 'completes the goal', just in a way that does not match the designer's implicit expectations.80% similarUnverifiedThe phenomenon of models increasing their score by adding length is referred to in academia as 'reward hacking' and is a manifestation of Goodhart's Law.78% similarUnverifiedBehavioral tendencies in the unsupervised state are closely related to the problems of reward hacking and idle behavior in reinforcement learning.76% similarUnverifiedThe reward model is only an external proxy, and the model may learn "reward hacking"—that is, finding output patterns that earn high rewards but are not genuinely safe.74% similarUnverifiedCommon practices for mitigating reward hacking include introducing penalty terms, using multi-objective reward weighting, or incorporating scheduling fairness constraints into the reward.73% similar
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