We still need third party training run assessments!

Recently, both OpenAI and Anthropic announced voluntary commitments to "pace the frontier". Among other things, they committed to having third-party "embedded evaluators". By default, I assume that this means more METR-style auditing of agent transcripts to identify cases of misalignment.

To be clear this is great! But I think it is insufficient. This is because misaligned transcripts are good evidence of misaligned models. However, the converse is not true; aligned transcripts are not (by themselves) good evidence of an aligned model! E.g. alignment audits by labs consistently fail to capture novel forms of misalignment.

Understanding whether a model is aligned or not, requires putting a model's behaviour in the context of how it was trained. Aligned behaviour could be evidence of a broad benevolent aligned disposition, or it could be evidence that someone in post-training chose your evaluation to hill-climb. As stated by Ryan Greenblatt recently:

I do not find it encouraging to see various specific misaligned behaviors go from a high rate with GPT 5.6 to ~zero with Astra. This seems indicative of wack-a-mole / papering over specific problems rather than solving the underlying misaligned drives.

Generally, evaluation data is the best evidence when we are confident that this is out-of-distribution from the training data, c.f. having large train-deploy mismatch.

So tl;dr I think a comprehensive alignment assessment has to include data about both (i) evidence of aligned / misaligned behaviour in various contexts; (ii) relevant details of the training procedure that determine how we interpret this evidence.