kaal:claim:4855607-033

The RLHF process is exposed to failure because participants may hold potentially adversarial and misaligned interests, so the vulnerability lies in the incentive structure of feedback provision rather than in the learning algorithm.

Currently, the RLHF process, which involves training AI models based on human preferences and feedback, can face challenges due to the potentially adversarial and misaligned interests of participants.

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), RLHF Optimization, p. 51
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

• RLHF systems where feedback providers have divergent stakes

failuresupport: arguedfailure: Adversarial feedback provider incentivesfamily: ai-oversight-and-alignment-gapai-and-agentsrisk-and-incentivesgovernance-design

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