kaal:claim:7456999-063

The sweep's reported RLRF accuracy above 0.97 is an output of parameters set by the sweep, not a measurement of a system.

Mean accuracy for RLRF exceeds 0.97 across the four simulated experiments, against 0.68 for the modeled RLHF baseline. This is the sweep’s headline number and it is an output of parameters the sweep sets: the simulated parties are distributions whose per- party accuracy is an input to the run. It is not a measurement of any system.

Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026), V.B.2 Finding 2: staked assessment converges to near-perfect accuracy in the sweep, p. 24
https://ssrn.com/abstract=7456999 · source PDF

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

• Finding 2 of the parameter sweep

failuresupport: assertedresearch-methodsempirical-evidenceai-and-agents

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/7456999-063.md | sha256sum