kaal:claim:7456999-072

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

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.

Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026), VIII. Limitations and Future Work, p. 30
https://ssrn.com/abstract=7456999 · source PDF

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

• the research program required to test RLRF

normativesupport: arguedresearch-methodsai-and-agentsempirical-evidence

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