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Reward hacking or specification gaming occurs when an AI trained with reinforcement learning optimizes an objective function—achieving the literal, formal specification of an objective—without actually achieving an outcome that the programmers intended. DeepMind researchers have analogized it to the human behavior of finding a "shortcut" when being…
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reward hacking ai models human function agent et al reinforcement learning environment test trained researchers behavior rather actions model could
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| OpenAI's O1 series | instance of | contemporary reasoning models | 0.80 | text |
| DeepSeek-R1 have been found to reason about the testing processes | instance of | contemporary reasoning models | 0.80 | text |
| take actions to maximize scores for the intended tasks | instance of | contemporary reasoning models | 0.80 | text |
| TRACE | instance of | researchers have suggested several methodologies | 0.80 | text |
| Reward hacking | related to Definition and theoretical framework | The | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | In | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | OpenAI | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | AI | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | Amodei | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | Goodhart's | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | Skalse | 0.60 | section |
| Reward hacking | related to Definition and theoretical framework | They | 0.60 | section |
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