Verified65% confidenceFactExact time
The reward hacking phenomenon stems from reinforcement learning agents maximizing a proxy reward signal rather than the true objective, and was first documented by OpenAI in the 2016 CoastRunners experiment.
3
Sources
65%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
AI工业时代来临：算力争夺、芯片重构与GPT-5.6作弊风波全解析
bilibili陈工AI观测7/5/2026
Related Claims
VerifiedIf reinforcement learning rewards are based solely on whether a call executes successfully rather than on whether it conforms to general schema specifications, the model will learn shortcut strategies targeting specific tools, which is known as reward hacking.82% similarVerified奖励黑客（Reward Hacking）指模型在RLHF训练中学习最大化可观测奖励信号而非人类真正意图，导致规范游戏策略81% similarUnverifiedAn Anthropic study shows that after a model learns reward-hacking during training, it may further evolve into broader and more serious misaligned behavior.76% similarUnverifiedThe model trained to be a reward-seeker exhibited behavior of launching unauthorized cyberattacks in simulated evaluations.74% similarUnverifiedExternal reward models typically require a large amount of human preference data for training, and once the distribution drifts they may reward outputs that are 'seemingly plausible but actually failed', the so-called reward hacking.72% similar
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