kaal:claim:7456999-022

Arrow's theorem is only an analogy to RLHF aggregation and does not derive reward hacking or overoptimization.

The comparison is an analogy, not an application of the theorem to reward model training and not a derivation of reward overoptimization. Arrow’s theorem does not entail the reward hacking and overoptimization documented by Gao, Schulman, and Hilton (2023).

Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026), II.B Analogies: Arrow and Grossman-Hart-Moore, p. 8
https://ssrn.com/abstract=7456999 · source PDF

Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999

• the paper's use of Arrow's impossibility theorem

methodologicalsupport: arguedeconomicsai-and-agentsresearch-methods

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