LLMs are not superhuman at reading and writing neural network weights
seangoedecke.com
seangoedecke.com
For example, an LLM's ability to comprehend and explain a complex codebase, that it's never seen before, far outstrips that of any human.
People like to come up with factors to quantify how much more efficient LLMs are. A newly hired human developer working on a complex codebase can take months to get up to speed, to get a holistic understanding of how it works so that they're able to make non-trivial changes. An LLM can do it in minutes. That's a factor on the order of 10,000x.
It may not be "communing through some Matrix-like symbology," but at some point it starts to seem that way.
(One possible disconnect: each row in the grid is one game, so Pac-Man is the third row, not the third image.)
https://inst.eecs.berkeley.edu/~cs188/textbook/rl/mfl.html
(material: https://inst.eecs.berkeley.edu/~cs188/sp26/projects/proj1/)
I think that after seeing that layout, plus the fact that the prompt mentions game and neural network training, you would have answered that the most likely game was Pac-Man :)
the other one where it was asked to figure out what language it was trained on, it was also give a choice between the 3, or they were indeed mentioned in the prompt
Game: <the game, as specifically as you can; if it resembles a well-known game, give that game's name>
This is why the distinction between open weights and open source is important.
And do that for how long?
The initial reporting/discussion surrounding these "LLMs understand LLM weights" made it sound as if these models had a superhuman ability to natively read and understand weights. Something that immediately seemed wild because I don't think that LLMs are trained to do that, so it would be an emergent ability.
If you look at the details though, it's fairly standard reverse-engineering: identify unique traits of the machine code you're looking at and make educated guesses as to what the code does, then use this understanding to unravel the next bit of code etc...
True: they are superhuman at every one of these tasks, in the boring way they are better—according to a wall clock and the coupled standard of actual results—at other things, just not in some mystical way.
> It’s cool that Astra was able to make direct weight updates. But we shouldn’t think of this like a radically new capability that will change how AI research is done. We should think of this as analogous to the ability of LLMs to emit machine code and tweak compiled binaries: it’s an impressive trick, but the best way to work with binaries is still to write source code and compile it.
It's like people are immediately forbidden to be impressed that AI was able to do this. The original article itself mentions the mechanisms by which Astra achieved this. They just of course wanted to gain more attention so they used a sensational title, like 90% of scientists that want their stuff read.
How is it not moving goalposts, it knew how to detect which weights to update and a way to do it? Even this article says so in the part that I quoted. The original piece never stated humans are unable to do this, I don't even think it implied it. So being impressed with this accomplishment is actually *ok*.
> I'm shook. If you'd asked me this morning whether LLMs have native understanding of their weights that is less than human <-> neuron understanding, the same as human or more than human, I would have said "same to less" depending on definitions.
> No human can go edit a brain to become better at math. This is just very, very surprising to me and extremely interesting. I would guess this upsets quite a number of interp theories and philosophical takes on what's going on with these networks.
> How is it even possible though I can’t think of any reason why they would have the intuition.
> This is the "recursive self-improvement" they've been warning about.
> I find this just completely shocking. I’m definitely skeptical. It just doesn’t line up with my understanding of how these models work. Is there really enough training data on direct weight editing to give a model this skill? Maybe it’s evidence that OpenAI has been creating a lot of training data on ML research. I’m surprised that was put into a public facing model though.
> I’m usually quite pro-AI, yet when I read this my first thought is that this is the first concrete sign that the world is about to end.
I think the paper was deliberately written and framed to elicit these kinds of reactions, and it is completely legitimate to push back on that.
Have you seen some of the things the model did in the paper? It edited weights directly with no tools.
I don't think this is something I've seen a human do or even seen hinted that a human can do. So I'd love to see a human do it. My guess is virtually no standard ML engineer could do this. Maybe some savant could, but I suspect that is what it would take.