{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/reward-model",
 "identifier": "kaal:entity:reward-model",
 "name": "Reward model",
 "termCode": "reward-model",
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 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
 "dateModified": "2026-07-29",
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 "subjectOf": [
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-035",
   "identifier": "kaal:claim:4855607-035",
   "text": "Applying decentralized voting and consensus to RLHF permits human feedback to be verified before it is used to calibrate the Reward Model, which raises the integrity and reliability of the feedback data entering the model.",
   "abstract": "Applying these mechanisms to RLHF allows for the decentralized verification of human feedback before it's used to calibrate the RM, enhancing the integrity and reliability of the feedback data.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "design",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "RLHF pipelines where feedback can be verified prior to reward model training"
   ],
   "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-038",
   "identifier": "kaal:claim:4855607-038",
   "text": "Gathering a wide range of human feedback makes the Reward Model reflect a comprehensive spectrum of human preferences and values, and it is this inclusivity that mitigates bias and captures a richer understanding of what counts as a desirable outcome.",
   "abstract": "By leveraging this model, RLHF can gather a wide range of human feedback, ensuring the Reward Model (RM) reflects a comprehensive spectrum of human preferences and values. This inclusivity helps mitigate biases and captures a richer understanding of what is considered a desirable outcome.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "reward models trained on feedback drawn from a broad participant base"
   ],
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   "status": "current"
  }
 ],
 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'reward-model'. Derived node: a roster, not an adjudicated definition."
}