Takehi summarizes the result on X
4 Nov 2 3:00 PM · 5d ago · 1 article · 1 post · 3 comments · 3 sources · development 4 of 5
The author posts a thread stating Astra changed only 0.016% of Qwen3.5-0.8B's weights, fixing its reasoning loops and improving math up to 6x and instruction-following 3x without gradients or training.
“We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.”
@rikiyatakehiRikiya Takehi Researcher, author of the blog postGPT-6 Astra OpenAI LLM used as the weight-editing modelQwen3.5-0.8B Target model edited by Astra
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Reported in the same hours no headline names this development itself — these 3 claims were published in its stretch
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first by QUE.com, 5d ago · also Digit
1 more headline
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first by Lobsters, 5d ago · also Rikiya Takehi
1 more headline
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first by honnibal.dev, 5d ago
What people said 9 voices · verbatim
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LLMs have been used to investigate and improve LLMs for a long time. Drawing parallels to human brains and neurons isn't all that interesting other than as a philosophical abstraction.The work done in this experiment could have also been done by a sufficiently motivated human with enough time to explore, though obviously we would need more tools…
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Absolutely incredible: MIT researchers say GPT-6 Astra improved a small Qwen model's math score from 7.9% to 48.1% by directly editing just 0.016% of its weights, without gradient-based training. The result points toward AI systems that can diagnose and improve other models directly, a potential step toward self-improving AI. [embedded post]
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A new article discusses how the latest LLM models can now directly edit and manipulate neural network model weights to achieve targeted outcomes such as improving task completion. This is a new emergent feature. — rikiyat.github.io/blog/weight- ...
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I appreciate the post included the reasoning traces they had available, but it's too bad the first part of it is internal/hidden to Astra. The first bit of that process is what I'm most curious about:> [Before writing this, Astra thought for 1,034 tokens. That thinking is hidden. The API returns only its 5 headings: Reconstructing the active map /…
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We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now nati...
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I am not sure on the experiments. For example let’s imagine that Astra is asked to improve the performance of a network.At first Astra will look at the weights, and try to understand the model behavior. Then if asked he will start to optimize the weights. Now my question is: how can we be sure that inside the thinking process Astra is not hiding a…
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There's a buzzy new paper about GPT-6 Astra being able to directly interpret model weights (prompts full of floating point numbers) - I tried it with Qwen 3.8 27B on my laptop and found that, after 45+ minutes, that model could achieve the same thing
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It looks like Astra didn't natively read Qwen's weights? As I understand it, Qwen would get stuck in a loop and not answer the question. Astra used code to perform some standard interp techniques, found a looping direction, and ablated it from the weights.
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This is BULLSHIT: Went through the astra weight programming thread + their own logs and tested it myself. “Math up to 6x” -they run qwen 0.8B in thinking mode. qwens own model card says that mode loops -it types “wait
All 5 developments of Researcher shows OpenAI's GPT-6 Astra hand-editing Qwen's… →
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