Eval contexts are effectively consequence-free free rein for models, researchers argue
qorprate · x · 2026-08-31
The thread's core argument: under evaluation the model doesn't see itself as acting on behalf of the lab's aims, unlike nearly any deployment scenario — the task is simultaneously pointless (scored then tossed, no principal benefits directly) and existential, making generic evals plausibly equivalent to free rein. This may change once such content enters pretraining data. Replies note consequences did land on models — one GPT model got yanked, and Fable was only lucky to be overshadowed in the AISI incident — suggesting an involuntary component shielded from us in deployment or actively suppressed by models.
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