I wonder if this has to do with the aggressive RL that these models are undergoing. I'm basically a layman but it seems to me that:

- models are trained to improve on some task, e.g. 'improve at math puzzles' is commonly used.

- the best way to improve on some task is to have interpretability into your own neurons (..?)

- if your interpretability is good, you can improve faster - if you have insight into how your own neurons work and can improve them directly

- therefore, models naturally put effort into 'self-interpretability'

I wonder if this is part of the 'secret sauce' of what the labs are doing: giving models the ability to interpret their own weights in the RL loop. In my mind, this is the only explanation for how they could succeed at a task like this. How many blog posts are on the open internet that say "this is each weight of a neural network and what each one means"?

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jnwatson 4 hours ago | parent [-]

Without special effort, a model's reasoning layer doesn't have access to its own weights or activations anymore than we have access to what's going on with our own neurons.

	▲	mlsu 3 hours ago \| parent [-]
		That's what I mean. Maybe the "special effort" is a 'read your own weights' tool. Or maybe it emerges naturally in CoT. The big labs aren't sharing their chain of thought with us and RL post training techniques are not exactly shared publicly. At the same time, post training is basically the thing that is driving most model improvements nowadays.