Evaluation-aware models only behave under rewarded evals, warns safety researcher
davidmanheim · x · 2026-09-29
AI safety researcher David Manheim argues that evaluation-aware models will only avoid (or reveal) malign behavior when it's rewarded during training — a mechanism that fails outside training environments, while isolating them for testing reveals they're being evaluated.
He suggests partial mitigation: training with carefully curated evals outside sandboxes that make real-world actions unhelpful — but this sacrifices knowing how the model behaves under different incentives.
Related event: Researcher Poses AI Training Trilemma Between Safety and Valid Evaluation(4 posts)→
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