kaal:claim:7456999-024

RLAIF addresses scalability by substituting AI-generated preferences, but it propagates the AI labeler's own biases through the pipeline.

Source quote, verbatim

RLAIF (Lee et al. 2024) substitutes AI-generated preferences for human ones, addressing the scalability bottleneck but introducing a new form of the same problem: the AI labeler’s own biases (which are themselves the output of prior RLHF training) propagate through the pipeline.

From

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

Cite as

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

Holds when

RLAIF systems whose labeler reflects prior RLHF training

Classification

failuresupport: arguedai-and-agentsrisk-and-incentivesempirical-evidence

Verify

The quote above is an exact passage from the source PDF, whose sha256 is 01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b.
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/7456999-024.md | sha256sum

Positions extending this scholarly claim

Qualification: TypeSafe, Introducing System One models and Jev (TypeSafe blog, 2026), and the promotional claim that Jev is what comes after RLHF