Posted by dbreunig 1 day ago
Have the AGENT interpret and edit it's OWN weights WHILE it runs. Make it so that this is how an agent learns and improves.
Some of this is because "the Bitter Lesson" type experience, but some of this is because of the idea that "we're making machines to do things we don't know how to do" bakes in inscrutability into the very definition of the field.
I was excited about the "Neural Networks Are Already Clean" piece not because alignment, which I don't think much about as a non-EA, but simply because we can do better engineering with whiteboxes than blackboxes. Now, here we are, doing it.
> [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]
This might just be an instance of "A working Nintendo in ~1GB of Qwen weights" from 3 days ago, human-made, than what everyone expects.
https://news.ycombinator.com/item?id=50306442
UPD. I just asked Astra (max) to attempt to do that for https://github.com/lostmsu/TurboGPT (a tiny transformer that trains in under 1min on RTX 5xxx series), and the produced weights were much much worse (bpb 4.47) than the trained ones (bpb 2.53).
Reasoning bit: "I’ll ... encode more specific rules directly in the weights." -- exactly the approach from above.
E.g. no recursive self-improvement here. Still impressive.