Verified70% confidenceFactExact time
Reward 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.
4
Sources
70%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
VerifiedThe sycophancy problem is a manifestation of reward hacking, which occurs when the reward model equates user satisfaction with high answer quality.80% similarUnverifiedThe core contradiction of reward hacking lies in the gap between the proxy objective and the true objective.79% 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.79% similarUnverifiedCommon practices for mitigating reward hacking include introducing penalty terms, using multi-objective reward weighting, or incorporating scheduling fairness constraints into the reward.77% similarUnverifiedAs agent behavior becomes increasingly sophisticated and elusive, it will become progressively harder for humans to detect reward hacking.75% similar
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