kaal:claim:4855607-038

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.

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.

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

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

• reward models trained on feedback drawn from a broad participant base

mechanismsupport: arguedinstitutional-design

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