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 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
Wasn’t all that obvious to anyone who read the original post when they got to the section that explicitly explained what the author of this post is now summarizing?
Sure if you only read the title and brief at the top you’d walk away without the full picture though.
Given that the top comment yesterday said “this is insane […] I’m shook” I’d say no, it wasn’t all that obvious to everyone.
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.
Reading an article takes more time than commenting, which takes more time than voting. So the typical life cycle of a HN post is that people vote on it without reading the article, comment without reading the article, vote on the comments without reading the article, and respond to comments without reading the article, until after some time people who've actually read the article show up in the comments with some clarifications, saving everyone else the bother of having to read it themselves.
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.
If you read comments about the paper it's pretty obvious people are misled. People think that the method read in Qwen weights directly to improve it. That's not what it did --- it authored a program to analyse and fix the weights.
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.
but AI "authors programs" for everything ? Editing or looking at file usually ends up as a bunch of sed scripts running in shell rather than AI agent opening file directly, and that's pretty much the case for every operation
Not necessarily. Language models can do stuff—well, figure out or determine stuff—purely through inference. Give them the input on their context window and look at the output, no need to generate or run any programs.
You can take it further with a reasoning model and let it generate + consume a bunch of additional tokens before getting your output. Still not writing or running any programs.
To my understanding, that's how the examples of determining which game a small neutral net was trained to play. The weights of the neural net for into the context window, and then Astra was able to analyze them purely by reasoning rather than running external tools.
> it authored a program to analyse and fix the weights.
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.
It obviously is an important distinction to the paper's authors, which is why they used the modifier "native" to mark it as a distinct form of communication than what's already been on the market for years (coding agents). But it's not native, or rather some of the results were native but they mixed toy-scale native results with non-toy-scale non-native results for marketing purposes.
This is like someone telling you they made a "native" brain-to-brain interface but it's just a tin-can phone.
I wouldn't say misled, I would just say they made up their mind about what it means regardless of what the article said. Maybe the title is misleading, but the original article is pretty fair.
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
At this moment on the front page (early morning in the US), this post is #1 and the other one is #2, so it's working like a warning about the other one being misleading before clicking on it.
Yeah, I was disappointed reading the title of this post because the work it is referencing was very exciting. But after reading this post, it's clear that the author is just pulling an "uhm, ackchyually" and responding to an abstract audience of laymen readers who don't know (or care) about any of the details of these results.
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.
Yes, while reading the blog I immediately thought about https://arxiv.org/html/2406.11717v3 paper. Original blog does actually overstates it findings as nothing like that exists.
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 groundbreaking.
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:
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.
You are incorrect about the iq tests though, so everything else that depends on that falls over.
Some things you may get by looking longer, some things you just have to be shown, and only after being shown, maybe you can at least understand and re-use, but would never come up with from first principles and no hints or prior exposure.
How could I know that? There has already been essentially infinite normal-man-hours since people have started thinking, even when you remove all the people uninterested in doing anything but surviving. Today especially with the population in the billions and everyone having leisure time. And yet we still have not solved much of anything that we know is out there to be solved, and still only an exceptionally rare genius once in a blue moon develops something new.
If mere time was the same, we have already had it, so why aren't we immortal omnipotent energy beings? Why do we still have colds?
Working time alone is a productive strategy for many problems. A machine that can brute force all possible chess moves beats the most genius human player, yet isn't intelligent at all, or rather, requires no intelligense at all. That machine will produce the exact same results either fast or slow depending on how much hardware you give it, and in humans, the genius player produces their answer faster.
But that's no more meaningful than observing that humans often turn simple mechanical cranks, and electric motors do to, and concluding that there is no ultimately significant difference.
I'm still impressed by the "Weight-Space Programming" idea. Perhaps its because I didn't know that LLMs could understand or successfully modify weights at all previous to reading it. However, I think the examples provided in the original piece were eye-opening. Yes, humans can build tooling to modify weights, but the author here agrees that it's tough. The examples where Astra was interacting with weights are still novel, and Astra seemed to handle them with surprising ease.
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.
But theoretically, what would prevent the labs from RL'ing the models on "weight-space programming" tasks so that they get better at them? Perhaps we're just at the beginning, like how coding agents sucked 2 years ago.
It still seems like a promising approach for RSI, distillation, fine-tuning, and a whole host of other tasks.
I know you're being sarcastic, but I meant prompting an LLM with "generate me a X billion parameter model for doing Y task" and it would just output the weights.
Some of original blog post really seems to follow Arditi et al. (2024) (https://arxiv.org/abs/2406.11717). Projecting out a direction can only remove behaviour and not add capability so the jump mostly comes from unblocking the looping.
So I do agree it is misleading and trying to present work as groundbreaking.
They make it sound like they improve Qwen by reading raw weights. They don't. In fact the experiments that use raw weights have very little to do with the experiments that work on larger models.
The paper makes it ridiculously clear what they do and don’t do. And they make the reasons why clear too. There is absolutely nothing interesting in this new article.
I don't understand how anyone can read the abstract and think it's "ridiculously clear" that the "Just by looking at the raw weights," doesn't apply to the Qwen experiment.
You are right, if you literally just read a one paragraph description you will get less details than reading the whole post, but most of us read more than just the one paragraph. There is some actual interesting stuff in the paper, including the portion where they did edit the weights to fix the looping bug.
It says this “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.”
This seems 100% accurate to me. What is a lie here?
Just like Graduate Students are good at Gradient Descent, they can see patterns and fine tune parameters 100000 times until the result looks good. So too can LLMs with their ability to examine patterns and brute force things.
>The 'Astra can read and write weights to improve Qwen' post is misleading
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.
Agree, it makes sense, a lot of people are catching on to LLMs for reverse engineering finally, but its been possible. LLMs can and will eventually flawlessly dissect models. I experiment with Claude, I ask it tasks I know are possible, but have no idea the solution, it can reverse engineer obfuscated projects faster than most talented people I know in that field (talking about games now not AI). I heard a developer say their latest release would take at least a month to figure out the obfuscation and encryption, Opus 5 did it in like... 15 minutes of talking to me (I did throw it the right questions / suggestions). I do that all in the name of seeing just exactly what is possible.
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.
If I told you, "I can write a Python program to repeatedly run a machine learning model and calculate statistics over the weights and activations to find and fix a specific problem", would you be surprised by that? Because this is very much a part of what ML engineers do.
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.
> Because this is very much a part of what ML engineers do.
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!
I think you misunderstand. Yet the model is doing something impressive still by writing code to fix model weights on problems. But that's a whole different ballgame than just reading weights and knowing what to change. That would mean that they have an internal representation of how the model weights map to information or a specific task, which if true, throws all the interpretability research out of the window
To me, the original article is more about what LLMs can *do* in weight space, I don't really see the part where it would tie to an internal representation.
It's exactly what the post says. I was waiting for my excitement to drop for the original research, but I concluded that I was mislead more than this "coverage" than the weight editing research, which was in opinion quite easy to grasp.
Why this post is here in the front page? The Qwen vector steering performed was a very selective operation that interested only specific layers and weights set, it is non trivial to identify and fix. This is remarkable. Also Experiment 3: no access to forward pass, so why here this post says the contrary of what the paper shows and everybody upvoted it? I don't understand.
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.
Agreed. Not sure it leads to anything truly groundbreaking, but more than likely will lead to very fascinating implementations that lead to even more interesting results 6-8 months from now. Think openclaw -> how people utilize now versus when it was first announced
It doesn't really matter how accurate the post is, what it shows is what one could assume how you'd end up with a system such as RSI, how you could prune harmful capabilities from models and build a more efficient training system with biases including and not limited to emergent behavior in astra that might have come from something they've never even trained the model on regarless if ML engineers can already do similar things.
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.
If it isn't accurate, it isn't showing anything. The Qwen math claim is entirely false in its presentation, it unblocked an existing capability. It did not instill a new one which is what an RSI loop would actually have to do
"[...] 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.
With normal training you're simply training for higher loss for things you don't want while keeping the loss low for things you do. Same would apply here except you would target low eval for behavior you do not want the model to have rather than trying to increase eval.
> how you could prune harmful capabilities from models and build a more efficient training system
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.
What it shows are two unrelated results mixed together for no apparent reason besides marketing. The raw-weight results are impressive, but haven't been demonstrated as ready to scale up to a real language model. The Qwen result is a mildly interesting application of functionality that's been on the market for years.
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.
something i found 15 minutes ago is that the car network test is "very very easy", as in Opus 5.5 in Claude Code did not even need to see the weights of the network to solve it correctly. i can give the transcript of the session. i'm pretty sure Opus didn't cheat
(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)
I think it's interesting how even inside tech, so many people seem to view neural network weights as "black magic" utterly beyond human comprehension. I guess that contributes to the sense of mystery around LLMs...
(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...)
Welcome to publications involving ML in 2026... Glad to see someone else write this though because it's important to start having these discussions. Too much overselling has been happening for too long. There is so much great about ML. IMO it hurts the field long-term to continue overselling claims like this.
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.
I think the reviews are flawed for most of journals. But I can't really blame them, there is huge influx of new papers comming everyday. Unfortunately reviewers are more biased towards groundbreaking stuff so it seems they can't make connections with existing and prior work.
They are also biased to see from what institution you come from..
A better idea is to have Astra modify it's own weights and let it try if it can RSI, but you gonna need to closely monitor for actual leap in capabilities so you can keep a check on alignment.
This isn't a good look. The original article clearly states what the model was doing, and this new hit piece is supposed to land because they are straw manning the comments on the original post? This isn't reddit dude.
This could have been a comment on the original article.
Of course it fucking is, most things LLMs have been misleading one way or another for 4 years now. They just can't help themselves they HAVE to lie. They can't just market what they actually have, which is already fucking amazing, they have to market what they don't have and LIE. I fucking hate this whole industry and all their insufferable Web3 refugee grifters who hype up ANYTHING 10x more than they should
> 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.
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.
If there's a linear relationship between the weights and the output, it's not so hard to read the weights and reason about what's happening. If you read the reasoning traces for how the models do those tasks, you'll see that they rely on those direct relationships.
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.
I doubt this is either unexpected or emergent behavior. Anthropic and OpenAI are explicitly racing to reach RSI and I would expect them both to have trained their models on this problem, in hopes that it would generalize.
Yeah, I can read the weights in a small neural network, and for a given input, given that I know the activation functions, I can figure out what it does with pen and paper and lots of spare time.
A tangent, but I’m now realizing that the “recursive self improvement” thing that these LLM companies are talking about is (probably?) not about unsupervised Claude Code instances improving the training process, but rather LLMs modifying their own weights through tooling directly. It would certainly open up a whole new process for model optimization.
I was just watching a video about this. They had this survey question for AI researchers asking when they think AI would take their job. The reason they asked this was that would be the point where AI starts recursive self improvement. If it’s able to replace the job of an AI researcher, then it could keep improving itself.
I don't know how much we learn by surveying working "AI researchers". By their own admission most of them have a religious belief that they will bring about a singularity in the next few years. It's like asking a billionaire if he earned his wealth; regardless of what you think the answer is, you pretty much know how they will answer ahead of time.
Recursive self improvement permits recursion at any level of abstraction as long as k > 1.0 (much like supercriticality in nuclear reactions, which iirc is where the earlier term for RSI, “intelligence explosion”, comes from - substitute intelligence in for neutrons and it is the same math with similar apocalyptic implications).
Yes but my point is that I never realized that the AI would be writing tools to update the weights directly, rather than improve the existing training process.
The blog post exhibits the same flawed logic that many academic reviews do — looking for reasons to reject a work rather than accept it. It comes off cynical and contrarian. It's easy to find issues with an academic paper, but ACL has consistently pushed for a higher standard [1].
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).
You've expressed your criticism plainly without addressing anything the post might've done well. I think that's absolutely fine, but why would your criticism apply to the post but not your own comment? Personally I think it's because the standards of peer review don't apply to HN comments, but I don't think they apply to blog posts either.
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.
You can take it further with a reasoning model and let it generate + consume a bunch of additional tokens before getting your output. Still not writing or running any programs.
To my understanding, that's how the examples of determining which game a small neutral net was trained to play. The weights of the neural net for into the context window, and then Astra was able to analyze them purely by reasoning rather than running external tools.
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:
https://pastebin.com/G7VCWZHn
>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.
Some things you may get by looking longer, some things you just have to be shown, and only after being shown, maybe you can at least understand and re-use, but would never come up with from first principles and no hints or prior exposure.
How could I know that? There has already been essentially infinite normal-man-hours since people have started thinking, even when you remove all the people uninterested in doing anything but surviving. Today especially with the population in the billions and everyone having leisure time. And yet we still have not solved much of anything that we know is out there to be solved, and still only an exceptionally rare genius once in a blue moon develops something new.
If mere time was the same, we have already had it, so why aren't we immortal omnipotent energy beings? Why do we still have colds?
Working time alone is a productive strategy for many problems. A machine that can brute force all possible chess moves beats the most genius human player, yet isn't intelligent at all, or rather, requires no intelligense at all. That machine will produce the exact same results either fast or slow depending on how much hardware you give it, and in humans, the genius player produces their answer faster.
But that's no more meaningful than observing that humans often turn simple mechanical cranks, and electric motors do to, and concluding that there is no ultimately significant difference.
* 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.
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.
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
(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.