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 traditional training. It improved in a lot of areas, but not enough to be usable for me.
Yesterday, the article the author of this post is critical of gave me enough of an aha that maybe the problems I was seeing could be solved by having a larger LLM change the weights directly, without going through training.
In the last twelve-ish hours I’ve made more headway than I have in 2+ weeks. I’m using one of my last well performing checkpoints (full fine-tune + changes to the decision head/LoRA, etc.) and gave the model free rein to change any of the layers.
Its last two actions: (a) removed 16 participant-order directions from a head matrix, which improved one type of reasoning and degraded another, and (b) restored one of the 16, which reversed the degradation while keeping the core improvement.
All that to say, this might be known to people who’ve been around training for years, even decades, but it’s new to a lot of us. And we’re excited about it.
>> The “just by looking at the raw weights” setting (raw floats in context, no tools, no running the network) only appears in experiments 1, 2 and 4, on networks of 17K–172K weights
Sure if you only read the title and brief at the top you’d walk away without the full picture though.
This is a better response imo: https://news.ycombinator.com/item?id=50344861
As someone who uses LLMs in their coding but doesn’t pay attention to every development this is why I find it all so exhausting. I keep feeling like I need to keep up to date but there’s no point reading incremental updates because everything is so overhyped.
If you want to escape the hype, reading posts with no upvotes and no comments on https://news.ycombinator.com/newest is an option, though somewhat hit-or-miss.
What's confusing is they do have experiments that read raw weights, and the abstract is misleadingly written to make it sound like that's how the Qwen experiment works.
This is a difference without much distinction, at least to me. It's like being upset when someone says that Claude made a http call -- of course the LLM didn't make the call itself, it used a tool to do it.
This is like someone telling you they made a "native" brain-to-brain interface but it's just a tin-can phone.
I think there's your answer. A hell of a lot of people will often read the title without reading the contents of the article. I admit I'm often guilty of that
Anybody who actually read the original research site was fully aware of the details of the experiments because it's a well-written research site that made everything very clear. This author is clearly just jumping on the hype as a sort of contrarian where they were frustrated with how much excitement the research was generating and wanted to "correct the record" for the set of people who don't matter in this space.
I'm fine with throwing the baby out with the bathwater in that case and just calling the whole article misleading.
But then you look at this complete trace generated by Kimi 3.1 when it tries to unravel the Breakout weights, and you see that it's bog standard reverse engineering, like any human would do, just vastly faster:
>This looks exactly Breakout: paddle agent at bottom row (row8), ball moving object anywhere, bricks rewards top rows, previous frame for ball movement. Agent weights bottom row, moving object weights rows1-7 (not row8? ball can't enter paddle row? Actually ball can), reward weights top rows1-3, obstacle walls. Previous frame used to infer ball velocity. No-op and directional actions move paddle left/right; up/down useless. Output biases similar all actions but W3 likely left/right structure. Could be Breakout. Should scrutinize output weights: actions 0 no-op,1 up,2 down,3 left,4 right. If Breakout, only left/right useful; network output weights should show action3 and4 distinctive and actions0/1/2 similar. Indeed W3 rows show patterns: [...]
> but ... it's bog standard reverse engineering, like any human would do, just vastly faster
For some values of vastly faster these are functionally equivalent and so equivalently groundbreaking.
That's the difference between me and Magnus Carlsen and Nigel Richards. Why downplay speed? I have a very, very hard time pinning down intelligence without time. I could basically do all IQ tests perfectly if I'm given infinite time, no? I never experienced one that is unsolvable, it's just that some people see it in a second or two and I and other lesser souls like me might need twenty minutes.
Edit: I'll add raw memory but yeah.. that's not hard to beat. Nigel's memory is insane as is Carlsen's, but compared to a computer it's nothing.
* Thinking Like Transformers” by Weiss, Goldberg & Yahav (ICML 2021).: https://arxiv.org/abs/2106.06981
* Tracr: Compiled Transformers as a Laboratory for Interpretability https://arxiv.org/abs/2301.05062 (code at https://github.com/deepmind/tracr)
So it's not really an "LLMs can modify weights" thing. Rather it's "LLMs can write programs that can write or modify weights". Indeed LLMs can write many programs, it's impressive.
It still seems like a promising approach for RSI, distillation, fine-tuning, and a whole host of other tasks.
Now that would be crazy
Now THAT would be crazy
So I do agree it is misleading and trying to present work as groundbreaking.
Most of us barely read the title
This seems 100% accurate to me. What is a lie here?
And when you see the reactions in the HN comment to the original article, it's pretty clear that most people have indeed been mislead.
The title is but then OP should talk about just the title instead
Weird, because they explicitly and clearly explain exactly what they did and didn't do.
Did you actually read the original post or just take the word of some HN comments at face value?
It's fair to say that this is malpractice.
Does the author mean the "LLMs can natively read & write neural network weights" post? Did the researchers change the name or something? Because when I read it, it made it very clear what they were doing and none of it was misleading. It all aligns with what this author is saying.
Claude has been able to do reverse engineering for nearly a year if not longer, as well as full source code rewrites, if you know how to do a proper rewrite, luckily for me, most of my career has been rewrites.
And then the experiment where it writes out the weights, in that one it's also writing a Python program to solve the problem --- which, again, is a program a person could write.
It's not nothing that Astra was able to perform these coding tasks, but they're still just coding tasks. They have nothing much to do with the experiment where it reads in the raw weights. In that experiment, the relationship between the input weights and the input is mostly linear, so it's a lot like doing image recognition. It's a cool result but it couldn't work the same way when you have complex transformations happening.
Do you realize most people aren't ML engineers?
I even built tiny neural nets myself and never played with the weights directly post-training. The original post was eye-opening for that reason. I knew it was possible to visualize weights, but as far as I understood, it was not really something I could edit with any success. Now, it seems possible without a phd just by asking another LLM. This is fun!
At each point the model has to write code to read the weights in with Python and some other Python code to perform analysis, like this: https://github.com/RikiyaT/lm_understand-logs/blob/main/exp3...
My complaint is that the post presents a confusing mix of experiments that imply capabilities that are more than the sum of their parts. This experiment 3 has nothing to do with experiment 1 for instance. In experiment 1 the model does read in the raw weights like it's reading an image.
You know exactly why... because anti-AI hysteria is quite popular on HN.
Any "critique" or "appeal to resist AI" rooted in emotion and inability to admit that intelligence is not a feat unique to humans anymore will make the rounds here.
You can let the AI generate training code for the next architecture too.
It's not like the AI is randomly losing its ability to write software just because it involves AI.
Big deal; have some bpf probe handle kick off of the rebuild when the when new weights are tested by some verification process
This is just CI/CD work now
None of the previous studies applies because Astra simply did not exist and this is worthwhile NEW knowledge that has been publicized and it is very interesting to see how it can be compared with other models as both openai and anthropic are keeping any and all RSI development completely private. The work is groundbreaking in a way that there will be a lot more experiments of this nature which will contribute to increasing the public knowledge of SOTA systems that was not normally discussed in model releases.
One could say is that we could now build a new benchmark which tests how many parameters a model can understand without any tooling other than eval or how complex of a system can be improved.
"[...] how you could prune harmful capabilities from models [...]"
We already do this, abliteration is the term that describes the pruning of capabilities or safeguards for models
"[...] not limited to emergent behavior in astra that might have come from something they've never even trained the model [...]"
If I was attempting to build RSI, I would have these exact types of problems in my RL datasets. There is no proof this is emergent behavior.
In theory, if OpenAI or Anthropic wanted to release a new open weight model, they could scrutinize it to remove the most horrendous things from the model.
If you think that the definition of "RSI" includes systems with a coding agent in the critical path, something humans can already do albeit faster, then your definition includes all of human history. If your definition includes AI systems that learn in a similar way to biological intelligence, then only the raw-weight results from this paper are pertinent. Those results are indeed interesting! This article is merely disentangling them from the frontier-lab-fueled hype that overran yesterday's thread.
The "The 'Astra can read and write weights to improve Qwen' post is misleading" post is misleading.
(opus <= 4.8 and gpt <= 5.5 couldn't match a PPO baseline network, and 5.6-sol and opus5 could barely match it. The 20x baseline improvement only comes from opus5.5 and sol > 6.1 / Astra)
(And also the sharp difference in levels of sophistication you see inside neural nets and outside of it: Pipelines that feed text to LLMs so it can detect highly abstract and nuanced semantic differences in the text's contents - but the pipeline still uses regexes for sentence splitting...)
There is going to be a reckoning for tech in the near future, because people will remember the lies and it's gonna be hard to beat the history.
In my field (biology+ML) it is a genuine epidemic. You could make a career out of comparing high-profile ML models vs baselines from the 90s.
edit: lo and behold! https://x.com/SchmidhuberAI/status/2117446822855888956
They are also biased to see from what institution you come from..
This could have been a comment on the original article.
The author is being equally disingenuous.
That piece by itself is insightful. Meaning, LLMs are capable of deciphering what a model can do by looking at the weights alone.
The fact that we can’t squeeze enough of them through the context is a technical limitation. However, this does not diminish the importance of this finding/discovery. This is an unexpected emergent behaviour.
The same could never work in a larger network. It's not about just capacity to feed in the weights, it's about the complexity of the relationship. There's no demonstration here that an LLM can do that, and it doesn't really make sense to want it to. It makes much more sense to author code to analyse and modify than it does to do it directly.
AI coding assistants make it much easier to conduct experiments and improve AI models, which in turn improves the coding assistants etc.
Being the author of spaCy, he should know better. I certainly enjoyed the straightforward simplicity of spaCy when I used it in my research (though I haven't touched it in years).
[1]: https://aclrollingreview.org/reviewerguidelines#-task-3-writ...
Trying to find every single angle and option to break something is how we verify the truth of something.
If something is objectively true, it will survive the scrutiny. If it does not survive, it wasn't objectively true.
This is how science works. That ain't no flaw. It is a flaw to be offended by science doing science things.