Thread debates whether this is a step toward recursive self-improvement
5 Nov 2 3:17 PM · 5d ago · 2 comments · 1 source · development 5 of 5
Commenters speculate the technique could be an early foothold for models improving their own weights, while others caution the editor model is vastly larger than the edited one and self-improvement was not demonstrated.
“This could be the first foothold on hill-climbing mech-interp.”
AlexErrantRikiya 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
The whole story articlespostscomments the bright band is this development · numbered dots are the others · click one to jump
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 HN Best, 5d ago · also HN Frontpage, Simon Willison, Simon Willison's Weblog
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first by HN Best, 5d ago · also HN Frontpage
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first by Latent Space, 5d ago
What people said 24 voices · best of 155 · verbatim
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[Might not be as big a deal as claimed](
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Respectfully to the authors and to everyone here, I ask you to actually read the paper before losing your mind, or at the very least plug it into Claude. It is a cool project but not as nearly as insane as people say. I think the blog writing and subsequent posting overstate the results.Part of my research is in weight space learning. For…
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This is a wild result. 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…
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Yes, the models are provable with math. Someone starts by thinking, I wonder what happens if I feed a model all of harry potter. Surprise it can generate new characters but is technically limited by previous grammar. So you consider folding in adjacent literature and you observe the model to have more variance. You see the pattern? We can write up…
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This means none provided by the harness, not no tools ever. Several of the experiments provided tools (python and other things), and the logs of both directions including what astra reported as reasoning summaries are linked on the blog post. internal python calculator use would likely tend to show up in the logs if that happened.
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Here is a measured take from Neel Nanda, who is quite publicly involved in serious interpretability research:> This is a cool paper, but I think it's best thought of as Astra is surprisingly capable of doing old school mechanistic interpretability research rather than anything spooky. The things it's doing are clever, but typically exploiting…
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After posts from Ethan Mollick on Bluesky, @ troed and others, about this blog and experiments that followed, has taken Recursive Self Improvement a leap forward? If AI can starts "fixing" AI without complete new training etc.? See attached posts. https:// rikiyat.github.io/blog/weight- space-programming/ https://www…
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Definitely not something I expected. This isn’t a human skill (or maybe a tiny group of people have this extremely niche skill?), and it’s not something these models are optimized for, but the capability somewhat exists anyway. The result is still limited, unsure if it can ever be viable on a larger scale beyond fixing some model issues, but the…
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The fact that astra had access to tools doesn’t really reduce the impact of this. The question is what can it do if you load it up with tools and enough time to try to improve itself.
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I really liked that comment because before that I thought I was going crazy. A lot of the discussion around these experiments seemed to imply, if not outright state, that LLMs had developed an ability to natively understand and manipulate LLM weights, a sort of transcendental introspection into their own brains, which if true would be absolutely…
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If this holds up ( a BIG if), then this is a very big deal... "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…
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Hypernetworks are back? Maybe reasoning was all you needed to make hypernetworks work. "programming" a hypernetwork is sort of the same kind of task as coding, and reasoning was essential for coding performance.
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I used [Qwen 3.8 27B on my laptop](https://simonwillison.net/2026/Nov/3/qwen-can-interpret-model-weights/) to replicate the result with the prompts that ask the model to identify Pac-Man, Breakout, and Pong based on the model weights for a model trained to play their 10x10 simplified variants.
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Speaking for myself. I’ve been aware of a lot of concepts around training, but stayed away from it until very recently.A few weeks ago, I formed a hypothesis that I could get a smaller model good at the specific standards I have for my git commit messages, which are hard to get out of most LLMs today.I spent the last four to five weeks on…
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Vibe Coding & AI News to Use: November 2, 2026… Astra lets an agent read and write model weights to improve Qwen https:// rikiyat.github.io/blog/weight- space-programming/ Momentic launches agents that catch bugs by using your app https:// momentic.ai/ Next ctrl+alt+create live: Builder Night, Monday, Nov 16, 6 PM at Formula Brewing in Issaquah…
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Obvious question is, if an LLM can take apart the weights of another (simpler) LLM, can we make tools that do it for us in more rigorous ways? Edit: Next question is, how well does this actually work on sample sizes greater than 4?
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Further than that, aren't "weights" just another sequence of tokens? We see them as vectors, pass to the API or as strings into a chatbox interface. Is it so surprising that the high-dimensionality linear algebra machine crunches them and gives us back useful vectors that can be mapped to text?Isn't that what this whole technology paradigm is…
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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. This is... Pretty mind-blowing ngl https:// rikiyat.github.io/blog/weight- space-programming/
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Not really. I'm interested more in that it's another thing to add to my mental model of what LLMs can and cannot do - it turns out they can reason their way through evaluating a small machine learning model.
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I work in this field (mech interp research) and I agree, the original post is definitely misleading. Some overselling in research is normal but it's a bit much.First, the key result has nothing to do with making Qwen better at math, it just reduces reasoning loops.Reasoning loops are a simple problem: the model generates the </think> token (which…
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So, Skynet was here all along. GPT-6 Astra can read and write the weights of other models & improve them. This is some crazy shit that I kinda wish wasn't true... https:// rikiyat.github.io/blog/weight- space-programming/
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Pretty crazy, BUT... > astra-6 with no tool How can we be sure what this really uses under the hood? Neural nets are black boxes in general, commercial LLM-based providers are even blacker boxes.
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I tried to reproduce this with an independent test. I find Claude can interpret small networks reliably. However it can't reliably propose improvements to even small networks.I had Claude train three language classifiers for short words in four languages. The network was just over 10k weights with a single hidden layer.I uploaded 6 language word…
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CRISPR/CAS but for LLM models. https:// simonwillison.net/2026/Nov/3/q wen-can-interpret-model-weights/
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