kaal:claim:4855607-035

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.

Source quote, verbatim

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.

From

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), RLHF Optimization, p. 52
https://ssrn.com/abstract=4855607 · source PDF

Cite as

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

Holds when

RLHF pipelines where feedback can be verified prior to reward model training

Classification

designsupport: argueddecentralizationgovernance-design

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