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  "text": "The first research requirement is an end-to-end training comparison that separately ablates reputation weighting, the two-round split, debate, and Format D.",
  "author": {
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    "name": "Wulf A. Kaal",
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  "citation": "Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999",
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  "abstract": "What is required is an end- to-end comparison, speci\ufb01ed 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.",
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