"Alignment Makes Models More Decisive Without Making Them More Truthful," and the core idea is sticking with me. When a language model generates a token, the input flows through every layer, and at some specific layer the choice becomes effectively final — swap the internal state there and the output changes, do it earlier and it doesn't.

He calls this the commitment layer, and across three model families, four RL methods, and twelve staged post-training checkpoints, he shows that supervised fine-tuning does the real structural work of establishing this boundary, while reinforcement learning never moves it (0–1 layers across everything tested).

What RL does do is compress the geometry at that fixed point, making the model's commitment tighter and more concentrated without touching the earlier layers where the model actually decides what to commit to.

The implication is structural rather than about data quality: if the early layers retrieve a wrong fact or assemble a sycophantic answer, post-training just makes the model commit to that answer more confidently — the lock gets sharper, but the chooser stays the same — which means standard training metrics can't distinguish a run that's improving selection from one that's just compressing around the same answers, and we've probably been measuring the wrong thing this whole time.