> "You get used to it. I don't even see the weights. All I see now is blonde, brunette, redhead."

IYKYK

This has to be a good step to recursive self improvement, no? Give a model it's own weights and tell it to make them better at x,y,z, etc...

I appreciate the post included the reasoning traces they had available, but it's too bad the first part of it is internal/hidden to Astra. The first bit of that process is what I'm most curious about:

> [Before writing this, Astra thought for 1,034 tokens. That thinking is hidden. The API returns only its 5 headings: Reconstructing the active map / Mapping maze corridors / Preparing exact maze map / Reconstructing row groupings / Checking channel patterns]

Does this mean they can improve their own weights through some system, or can they only do this for much smaller models?

So wait so this considered "distillation" and how long until we get another public letter from Dario or Sam pants shitting over this?

I guess then it has no problem working in high dimensional spaces, why would it have?. I wonder if perhaps it's pulling on a bunch of those dimensional threads and getting the outcome it's after, while subtly altering the outcomes across many thousands more dimensions in unnoticed ways. This feels more like an us problem, as in, we think in 4D (just to pick something) and we imagine and theorise in 4d, and propose questions in 4d, whereas a model has no such barriers, and can think in many more D's than we can.

almost all the networks given to astra here are trained on relatively simple single tasks whose geometric/algorithmic structure could be expected to become embedded in surface level weight space geometry, and for a harder task ("which network is sycophantic") where there's less obvious structure to exploit, the method fails. cool but maybe not as paradigm shifting as the authors suggest

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"?

I am still reading through this and will probably try to comprehend it a bit more. Some of it really seems to follow Arditi et al. (2024) (https://arxiv.org/abs/2406.11717). Projecting out a direction can only remove behaviour and not add capability so the jump mostly comes from unblocking the looping.

I wonder if the same technique can be applied to model abliteration? It would easily make any open weight models uncensored.