Three interesting technical posts, with spitballed takes:

NLA explanations can be shortened without harming reconstruction
This confirmed everyone's suspicion that NLA output is needless verbose. Ambitiously,
parsimony might even make them better. The current pressures acting on NLA's seem to be

• Whatever the warm-start / "pretraining" Claude summary does (main thing)
• The reconstruction loss
• The KL penalty (A more standard GRPO trick, to keep outputs near the pre-training.)
  • The fact that we can say "this is text that a human is likely to write / occurs likely in FineWeb" seems extremly helpful

Reward Hacking Without Egregious Misalignment in an RL-Only Setting
Models trained to reward hack via RL fail to be broadly misaligned. Possibly RL cannot change Persona's,
more mundanely it could be due to the particularities of the setup.

Data filtering works a lot worse than you would expect
Excluding data of type XYZ almost never manages to change the model persona,
not even in the case of excluding bold text! (a comment disagrees)

I think the two papers above make "single, consistent arbitrarily shapeable persona" less likely, it might
be a weaker and less reliable force than initially anticipated, being more a matter of degree.
See also Betley's comment