# kaal:claim:7456999-001

**Claim.** RLRF replaces the human annotator panel of RLHF with an economically incentivized validation pool of computative agents whose members stake non-transferable reputation.

**Type.** design  **Support.** argued

**Holds when.**

- the RLRF mechanism as proposed in this paper

**Source quote.**

> This paper introduces Reinforcement Learning from Reputation Feedback (RLRF), an alignment mechanism that replaces the human annotator panel of Reinforcement Learning from Human Feedback (RLHF) with an economically incentivized validation pool of computative agents whose members stake non-transferable reputation.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), Abstract, page 1

**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.** ai-and-agents, reputation, risk-and-incentives

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