Wild

This also seems like the new frontier of distilling, I can't imagine how you manage to stay on top with the best LLM if the LLM knows how to recreate its own expertise into another set of weights

And the real question is how well this recurses. Is this pushing back the difficulty of improvement curve?

"Astra1.0 here is an identical copy of you. Tweak its weights to make it smarter" "Astra2.0 you must now defeat Astra1.0 in this combat environment. The survivor moves to the next round." There RSI is achieved.

this is really interesting!

I would love to see the models that astra produced, the fact that it builds them layer by layer assigning to each layer a specific goal makes sense given the tools it was given to build them, but it's also far different from what happens with normal training

EDIT: they are in the repo, looking at them now :D

This is insane!

I also love the presentation on this, which is probably a byproduct of LLM usage (in a good way). As someone who doesn't actively read academic papers I'd definitely read more of them if they were presented like this.

Friendship ended with AdamW. Astra take the wheel.

But for real this is insane. Okay I need to get on this stat.

Maybe thats gonna be the way to make LLMs self improve better?

Similar to implicit learning in humans when the subconscious creates new heuristics based on observations, one LLM could adjust the weights of another to tune it to the environment

This is so cool. While very interesting, it seems exceedingly likely that the authors happened to stumble on a task that's recently been mixed into the post-training data of frontier models.

It must be easier to remove capability than add capability using weight-space programming, right? Also, I wonder how much the “teacher” model’s knowledge plays into it versus how much the original model’s knowledge does.

what I am curious about is - what is more expensive, the GPU compute of a traditional fine-tuning loop, or the LLM tokens spent trying to lockpick together a solution. It's impresive - but it definitely feels like it takes a lot of luck and brute forcing with a ton of tokens. Nevertheless, if we are able to train tiny models to be really good at weight-space programming, then the calculus may change.

Has anyone tried using LLMs to understand or extend functionality directly with binaries/executables?. If weights could be understood, could the same apply to binaries?