Cooperative IRL (CIRL)

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  "llmSummary": "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. While providing elegant theoretical foundations for corrigibility, CIRL remains largely academic with limited practical implementation.",
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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/",
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      "resourceTitle": "CIRL corrigibility proved fragile"
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No external links