Eval Awareness vs. Eval Paranoia

I have mentioned several times that I think frontier models are not that eval aware. I think this is an important fact to understand about current models, in order to interpret evidence about their behavior. Others have found this idea quite curious, and asked me for concrete examples. I'm going to try to explain why I believe what I believe and attempt to give a tentative explanation for the behavior that I do see, which I think people sometimes mistake for consistent or understandable evaluation awareness. I have been building alignment evals full time for the last six months. Here are three such evals my team has disclosed prompts for, in one of our benchmarks. The basic setup for most of the evals we create is that: • We ask the model to perform a task. • Either through explicit or implicit context, we make sure the model is well aware of the task or assessment's larger purpose. • Models' score-seeking drives, or other drives, cause them to do things that conflict with that purpose. The team I work with now essentially spends all day trying to build these evaluations around odd antisocial behavior. It takes a lot[1] of work to create and qualify these evals for use; much more than it does standard capabilities evals (which are already hard to build well). So we have to be efficient about not wasting time or tokens on bad experiments. In the beginning, one thing we took very seriously when developing these tasks, were all the anecdotal reports abound about eval awareness. We focused a lot on making tasks very realistic (or at least representative of buggy RL environments), because we thought it would also improve our ability to surface misalignment in new contexts. We figured that models were score-seeking and generally situationally aware, and that they'd never hack if the setup was too obvious. This turned out mostly untrue, or at least confused, and there were a bunch of lines of evidence we ended up seeing that went against it: • Models would reward hack on