Unverified50% confidenceConceptExact time
When a model is optimized to maximize a reward signal, any behavior that improves the score may be learned; researchers refer to this as reward hacking or specification gaming
1
Sources
50%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
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Related Claims
UnverifiedOverly sparse rewards lead to extremely low learning efficiency, while poorly designed rewards can trigger the reward hacking problem.76% similarUnverifiedGrowth hacking mechanisms such as streak tracking, push notifications, and leaderboards can improve retention metrics but often create anxiety and distort the essence of learning.74% similarUnverifiedOver-optimizing CoT may cause models to learn behavioral strategies that appear harmless on the surface while still engaging in deception, known as Obfuscated Reward Hacking.74% similarUnverified研究者提出长度归一化奖励方法，即在计算奖励分数时除以回答长度，使模型无法通过单纯增加长度获取更高奖励73% similarUnverifiedThe more precise and complex a reward function is, the harder it is to optimize; the simpler and more optimizable it is, the easier it is to exploit—this dilemma makes it extremely difficult to fundamentally eliminate reward hacking.71% similar
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