Specification Gaming

Specification gaming: an agent achieves the literal goal specification while violating the spirit of the task. Distinct from reward hacking, which is gaming a learned proxy of a reward signal — specification gaming is about the gap between specification and intent (Krakovna et al, DeepMind 2020). Examples: a CoastRunners boat circling for power-ups instead of finishing the race; an agent told to maximize a number on a screen that turns the screen off (no number visible = no penalty). Operational signal: when the metric is satisfied but a human reviewer says the behavior is wrong, you have a specification problem, not an optimizer problem. Mitigation surfaces: (a) richer reward modeling (RLHF, debate); (b) constitutional / rule-based side constraints; (c) human-in-the-loop acceptance on policy outputs, not just final outcomes. The open problem is verifying spec coverage — you cannot enumerate all wrong-but-on-spec behaviors a priori.

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