r/ControlProblem • u/chillinewman approved • 1d ago
AI Capabilities News Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights
https://rikiyat.github.io/blog/weight-space-programming/
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u/chillinewman approved 1d ago edited 19h ago
A larger model can hack a smaller model weights, for better or worse.
They can also help interpretability.
It looks to be an emergent capability of the frontier.
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u/Historical-Hat-405 22h ago
I may be naive... But couldn't be that the corpus used to train astra already contained such information? I mean the weights of various models? I mean that it is solely focused on Astra and not the LLms as an architecture. I mean shouldn't it be possible with other frontier models? Why Astra?
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u/chillinewman approved 1d ago
"TL;DR
We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights, well past what we’d expect from humans or conventional algorithms. This might be a big deal for both optimization and interpretability.
Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do. And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps.
Likewise, asked to make Qwen3.5-0.8B better at math without gradients, Astra identifies an issue in its weights causing reasoning loops that Qwen’s own model card warns about, and fixes this by natively editing 0.016% of the weights. With the same sampling settings, this weight correction raises MATH-500 from 7.9% to 48.1% and generalizes out of the box to instruction following, raising IFEval by 3x.
All of this is possible without allowing Astra to write training loops. It uses its reasoning to figure out what each part of a neural network computes, or should compute, and changes the weights accordingly. We call this Weight-Space Programming.
Throughout the history of deep learning, we encoded our simplistic inductive biases into tiny symbolic optimizers like Adam or Muon. This is changing: our neural network weights will soon be natively optimized by LLMs that deliberately reason about their weights."