It does, one of my ongoing research papers is about it. But I would ask people to take a look at the established idea of Hypernetworks: https://arxiv.org/pdf/2306.06955v3 Modern version of the field comes from Google Brain around 2016 https://arxiv.org/pdf/1609.09106.

In general my personal research thesis is that current deep learning is much less malleable and neuroplastic as it should be, I got into interpretability because of this. Many focus on safety, but I prioritize it as a toolbox that allows for greater shaping of the underlying intelligence system. If we can fully understand how learning happens, where information is stored and how it is processed, unlike in biological brains, we can apply edits and transformations in digital ones. And if I can use a technique to edit weights based on some underlying reasoning about a system (with a caveat that it leads to something worthwhile), so can an agent. Hence self-improvement. There are many interesting implication to weight space research, and I very much encourage people to read about it. But it is also very easy to overstate what is currently possible, in fact I'd say better to know what is not so one can make it possible.

>current deep learning is much less malleable and neuroplastic as it should be

Why do you think this is? I am very new to this research, but to me, the biggest challenge appears to be how information seems to be stored in superposition (making it difficult to change one thing without affecting others). But biological brains appear to exhibit the same kind of superposition storage as well, and yet they are very plastic.

Not who you asked, but biological brains come about very differently than LLM neural networks. Most notably, they come about largely through plasticity, and with evolutionary pressure towards exhibiting plasticity.

My guess would be that the structure to allow for plasticity to work is a strong limit to how well you can tune weights to fit training data. My (doubtless massively oversimplified and way behind the state-of-the art) understanding of neuro-plasticity is something like "often used pathways get stronger connections". Which, even with significant tweaks, is quite different from the gradient descent used to tune neural networks.

If strengthening of frequently used neural pathways is all our brains do, I don't see how it's seemingly capable of reordering knowledge physically from one part of the brain to another (which I believe is the case). Infrequently used pathways would simply rot away and die away over time, rather than being moved somewhere else for long-term storage to keep the high-frequency pathways more available.

If you haven't seen it, this recent blogpost might interest you: https://news.ycombinator.com/item?id=50105647

It argues that our understanding of neurons storing information in superposition was just a limitation of our tools and us thinking about it wrong. Instead most neurons initially specialize, and then branch out into related concepts. In addition, understanding one part of the network helps better understand other parts. And with agents we can scale this to understand non-trivial networks.

While, if true, this would more directly help with interpretability, a better understanding could also help with improving NNs in general.

This is not "self-editing". We're comparing Astra to a model with not even 1B parameters.

I'm reminded of how humanity recently finished mapping out the fruit fly connectome; this does not put us meaningfully closer to, say, doing surgery on humans to cure schizophrenia.

I totally agree, but this is still super cool, and I'm really happy that the hype around this paper brought this line of research to my attention.