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  "text": "The paper withdrew its earlier Cramer-Rao efficiency conjecture because revisions could not make it coherent and comparable with deployed RLHF, and it makes no mean squared error claim.",
  "author": {
    "@type": "Person",
    "name": "Wulf A. Kaal",
    "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "citation": "Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999",
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    "name": "Reinforcement Learning from Reputation Feedback",
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  "abstract": "Earlier versions stated a Conjecture 1 on Cramer-Rao ef \ufb01ciency dominance of the RLRF reward estimator over the RLHF reward estimator. It is withdrawn in Version 14. Successive revisions could not make it both internally coherent and a comparison with deployed RLHF. The paper makes no mean squared error claim.",
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