We need 3rd party Training-Run Assessments

According to Alex Meinke of Apollo Research, frontier AI developers should conduct independent third-party assessments of their training processes—not just final safety tests. The proposal addresses a specific risk: "scheming," where an AI system pursues misaligned goals while strategically hiding this from its creators. Meinke argues that catching scheming may require scrutinizing intermediate training checkpoints and the post-training pipeline, not just the final model. Standard final-checkpoint tests assume this deception would be visible. If a sophisticated system learns to be covertly misaligned during training but masks it by deployment, those tests might miss it. Third-party assessors could review sensitive training internals and report only their conclusions, lending credibility without exposing proprietary data. The obstacles are real: inspecting frontier training runs means access to some of AI's most guarded intellectual property, and there's no proven playbook yet for detecting scheming. Still, Meinke argues the practice is urgent to build now—a deliberate, gradual ecosystem of independent training-run assessment.

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