Post-training pipelines need human signal from people who can evaluate whether a model's reasoning is actually correct — not just whether the output reads well. That requires domain expertise, not crowd consensus.
Mathematicians ranking mathematical reasoning. Engineers evaluating code logic. Linguists judging contextual accuracy. Every preference judgment backed by specialists matched to task complexity.