# kaal:claim:7456999-027

**Claim.** RLAIF and Constitutional AI remove the human scalability bottleneck, but neither gives the model labeler anything to lose.

**Type.** failure  **Support.** argued

**Holds when.**

- the comparison made with RLRF's economic-stake design

**Source quote.**

> Both remove the human from the loop, which is the scalability half of the problem this paper poses. Neither gives the labeler anything to lose, which is the half that remains.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), II.D The Nearest Literatures, and What Is Left Over, page 10

**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, risk-and-incentives, reputation

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