# kaal:claim:7456999-051

**Claim.** 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.

**Type.** failure  **Support.** asserted

**Holds when.**

- the withdrawn Conjecture 1

**Source quote.**

> Earlier versions stated a Conjecture 1 on Cramer-Rao ef ficiency 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 It is withdrawn in Version 14.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.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), IV.A The Withdrawn Efficiency Conjecture, page 17

**Cite as.** Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999

**Verify.** sha256 of source PDF `01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Reinforcement%20Learning%20from%20Reputation%20Feedback.pdf

**Topics.** research-methods, ai-and-agents

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