Training Away the Ability to Tell Evals from Deployment

TL;DR: We introduce Adversarial Blinding, a training method that degrades the model's ability to distinguish between eval and deployment, with the aim of making evaluation results more representative of deployment behavior. Our method reduces eval awareness more than activation steering, the current standard approach, while avoiding steering's known personality drift confounders. Authors: Francisco Pernice (Transluce) and Santiago Aranguri (Anthropic). This research was done while both authors were at Goodfire. Outline of results On Qwen 3.6 27B, Adversarial Blinding reduces eval awareness across our eval awareness monitors more than activation steering while staying closer to the original model in KL divergence, albeit at a small cost in general capability. We further validate our method by rewriting HarmBench prompts to look more realistic. Adversarial Blinding shrinks the eval-awareness gap between original and rewritten prompts 4x while steering widens the gap. Adversarial Blinding also generalizes to user awareness without being trained on it. Zhong et al. (2026) showed that when the user is a famous AI safety researcher, models answer certain prompts with lower confidence tha