# kaal:claim:7456999-069

**Claim.** RLRF is intended to make governance adapt through evolving reputation weights, but whether those weights track validator accuracy or transfer between keyed and unkeyed tasks remains unproved.

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

**Holds when.**

- the dynamic-regulation rationale for RLRF

**Source quote.**

> RLRF is intended to address this by making the governance mechanism endogenous. The reputation vector evolves as validators assess new outputs, and the design intent is that the reward signal tracks distributional shift without centralized re-annotation. Whether the weights at the reputation fixed point come to track validator accuracy against rather than merely tracking agreement with the realized pool resolution, and whether weights earned on keyed tasks transfer to unkeyed ones, is unproved and is the second open problem in Part VIII.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), VII.A Dynamic Regulation and the Pacing Problem, page 27

**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.** dynamic-regulation, reputation, ai-and-agents

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