Cooperative IRL (CIRL)

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  "title": "Cooperative IRL (CIRL)",
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  "summary": "CIRL is a theoretical framework where AI systems maintain uncertainty about human preferences, which naturally incentivizes corrigibility and deference. Despite elegant theory with formal proofs, the approach faces a substantial theory-practice gap with no production deployments and only \\$1-5M/year in academic investment, making it more influential for conceptual foundations than immediate intervention design.",
  "description": "Cooperative Inverse Reinforcement Learning (CIRL) is a theoretical framework where AI systems maintain uncertainty about human preferences and cooperatively learn them through interaction.",
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      "text": "Hadfield-Menell et al., 2017",
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      "text": "Cooperative Inverse Reinforcement Learning",
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    {
      "text": "Incorrigibility in the CIRL Framework",
      "url": "https://intelligence.org/2017/08/31/incorrigibility-in-cirl/",
      "resourceId": "3e250a28699df556",
      "resourceTitle": "CIRL corrigibility proved fragile"
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