# kaal:claim:7456999-072

**Claim.** The first research requirement is an end-to-end training comparison that separately ablates reputation weighting, the two-round split, debate, and Format D.

**Type.** normative  **Support.** argued

**Holds when.**

- the research program required to test RLRF

**Source quote.**

> What is required is an end- to-end comparison, specified in advance, in which policies are trained under a reputation-weighted signal, under an annotator or model-rater baseline, and under the selector that ships whatever passes the visible checks, and are then evaluated on held- out work that none of the three signals could see. The reputation weighting, the two- round split, the debate stage, and Format D should be ablated separately, since Part II.D concedes that each has a close neighbor in the existing literature and the question is what the combination adds.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), VIII. Limitations and Future Work, page 30

**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, empirical-evidence

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