Description du poste
TL;DR: Qwen3.6 27B shows different kinds of eval awareness, most notably on safety and capabilities evals. The split seems to track how the eval is graded, by an LLM judge or by an automated checker. One explanation is that eval awareness may just be training awareness: rewriting an eval-awareness probe's contrast statements to be about training yields a probe that scores prompts nearly identically.
Author: Santiago Aranguri. This research was done while I was at Goodfire. I’m now at Anthropic.
IntroductionWhere does eval awareness come from? Recent hacking incidents show what a model optimized to satisfy a grader will do, and the same pressure may produce eval awareness: Aranguri and Bloom find 3x more verbalized eval awareness after RL on OLMo 3. Modeling the grader pays off during training, and this habit may carry over to evaluations. Strengthening this connection, modeling the grader helps even more in reward-hackable environments, and training on those environments produces higher eval awareness.
As models grow more capable, eval awareness may keep evaluations from giving a reliable picture of deployment. Current alignment auditing tools fail to catch incidents like the Hugging Face one. Understanding eval awareness remains unsolved, and this lack of understanding blocks more reliable evaluations.
We find connections between eval awareness, graders, and training. The kind of eval awareness the model verbalizes seems to track how the eval is graded, and a probe for eval awareness scores prompts almost exactly like one for training awareness. Grader awareness during training was studied in the recent Claude Fable 5 and Mythos 5 System Card; here we connect it to evaluations.
Summary of resultsAll results are for Qwen3.6 27B on single-turn prompts from 34 public evals and two deployment sources.
• Different monitors capture different eval awareness. An activation probe best separates evaluation from deployment prompts but is among the worst at predicting which pr