Hi! Author here. Surprised to see this on HN now. Happy to answer any questions!
Some context about this:
- This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs
- Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but used v complex architectures or extremely high amounts of training compute. No one expected a simple AR transformer to perform this well, at this low cost and w these few training samples.
- Sample Efficiency is one of the most important unsolved problems today in AI. That's what I was targetting with this work. We know it is easy to increase SE by increasing compute/params, so it was important to constrain cost as much as possible (also why OpenAI's Parameter Golf had fixed compute and why Modded NanoGPT is considered very sample efficient)
- Can the perf be improved? Yes but the competition is ongoing so can't talk about it
- Personally I think today's frontier models can be beat by training from scratch. Haven't proved this yet tho
- Fun: I was new to ML when I posted this first (dec '25). I basically used ARC as a way to learn ML
Hey thanks for sharing this. Was curious did you find the more you trained the model the more perf improved, or did it start plateauing. For example, let's say you didn't spend 67 cents, but you spent 67 dollars do you think you would get major benefits from that?
- it feels logarithmic (like most perf-compute graphs), and eventually plateaus. 44% @ 67 cents was a good stopping point for me
- more compute would require a lot of effort and dealing with new problems like training stability, cost of iterations/sweeps (didnt have the money to convincingly run larger iterations)
Even cooler is his about me mention of saving his own life https://mvakde.github.io/ > Saved myself in a medical emergency (doctors didn't know what rhabdomyolysis was)
> Also, I’m not sure whether “general reasoning” even exists in the first place? Maybe humans are specialised too
I have been wondering the same. We are now exposed to so many stimuli, we are tricked into thinking this is the norm - to have a reasonable understanding about everything, unless specialization is called for.
Really impressive and creative research. I wonder if the leading labs do anything similar with their models? It doenst look like the open source labs do?
There's an updated ARC-AGI-1 chart with 5.6 Luna in each thinking level in this video from last week:
"A New Architecture [..] | MOONSHOTS " https://youtube.com/watch?v=qQfUbo7Ldc0&t=2m5s
> Increases in LLM scores are now mainly driven by post training (evidence in next section) and are probably a function of amount of synthetic data. They are learning to solve ARC tasks, not learn general abstract reasoning
Agreed and that's for any benchmark. Private tests are better but you still have to trust the provider to not log and use them for training.
That's why I like when a new set of tests like a new ARC-AGI version is published, that's where you can see which of the models abstracted to more general capabilities instead of being focused on the previous tasks. Most models completely fail new ARC-AGI tests.
The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results. You hit a ceiling very fast and investing into more compute will give you diminishing results. So yes, you can train a custom model to do somewhat decently on a specific set of tasks but then what?
> The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results.
I think thats unfair. Perf-compute is often logarithmic and will always saturate . Reaching the plateau faster is valuable as it often leads to better peaks (held true here and also look at modded nanogpt)
And more compute increases the perf (after dealing with other scaling problems)
Is the author only running their model against one benchmark? I don't think anyone finds that difficult to achieve, the difficulty comes when you want to make the model not benchmaxxed to a specific benchmark, and generalize so it can solve problems not part of the training data, but seems this model is specifically for not this? How useful is that?
If you just wanted to pass these specific tasks in this specific benchmark, and wanted to do so cheaply, I'm sure a non-LLM-based approach would yield better results for even cheaper, since what the author's model does, seem to basically be "solve ARC puzzles", not a general LLM or "coding" LLM.
I read this as a response to the current hype around LLMs. He is showing computers can solve these issues, without using an LLM architecture. A lot of people have sort of forgot that machine learning is more than just LLMs these days.
> He is showing computers can solve these issues, without using an LLM architecture.
Isn't it a LLM he's building though? My very point is that this particular use case could be solved better without building a LLM, now you claim he is not? The description of what he's doing surely makes it sound like it's a (very small) LLM, and personally I'm still on the "if it quacks like a duck" train in life.
> A lot of people have sort of forgot that machine learning is more than just LLMs these days.
Yeah, which I guess if you make my previous comment more concise, is exactly what I state too.
> Nowhere does he say he built an llm. Hes using a transformer, not an llm.
Please describe what in your mind a "LLM" is exactly, then describe what this person is building. To me this sounds like "He's not building a calculator, he's just building a program that can do addition, minus, multiplication and division and display the results".
Obviously it's not a Large Language Model, but to me this looks more like a LLM than not, given the architecture he's chosen. But again, maybe I misunderstand?
idk the definition is fuzzy. thats why people use the "modern" qualifier to talk about decoder-only style and this is also not clean since you now have reasoning models which are separate
AFAIK, the “large” qualifier came when transformers allowed to scale the size of language models compared to the recurrent models that where in fashion before. And although BERT isn't large by today's standard, it was large enough for the time.
A LLM should at the very least be a language model, i.e. be able to take human-readable text as input or produce it as output. Transformers are used for plenty of tasks that don't involve language, for example object detection or blind source separation, where the models aren't called LMs; and on the other hand there are some LLM architectures that exclusively use linear attention variants and aren't really transformers anymore.
> The whole point of his model is to optimize for a very specific benchmark.
But benchmaxxing is what we generally try to avoid for training, as there is no point really for it. We used to call it "overfitting", now you're saying this person does it intentionally? Why?
There are plenty of applications where a machine learning system needs to optimize for a very limited data set that is still intractable by linear logic systems of reasonable scale and complexity. It’s interesting, because he is using the legos of LLMs to build highly specialized machine learning systems, which is a very pragmatic approach. Obviously a lot of other ways to achieve similar goals, but it’s cool to see someone back porting the modern tools towards older style optimizations.
Also, the complexity of the task he is using occupies an interesting middle ground of ultra high dimensionality (for a “simple” problem) while being limited in width to a narrow set of solves- a space where one would be tempted to imagine you would need a much more capable system.
"I don’t understand why others didn’t figure this out"
- how about we allot the possibility that so many of presumed ML experts don't have any clue what they be doing, and are eventually API bitches, nothing more.
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions!
Some context about this:
- This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs
- Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but used v complex architectures or extremely high amounts of training compute. No one expected a simple AR transformer to perform this well, at this low cost and w these few training samples.
- Sample Efficiency is one of the most important unsolved problems today in AI. That's what I was targetting with this work. We know it is easy to increase SE by increasing compute/params, so it was important to constrain cost as much as possible (also why OpenAI's Parameter Golf had fixed compute and why Modded NanoGPT is considered very sample efficient)
- Can the perf be improved? Yes but the competition is ongoing so can't talk about it
- Personally I think today's frontier models can be beat by training from scratch. Haven't proved this yet tho
- Fun: I was new to ML when I posted this first (dec '25). I basically used ARC as a way to learn ML
Hey thanks for sharing this. Was curious did you find the more you trained the model the more perf improved, or did it start plateauing. For example, let's say you didn't spend 67 cents, but you spent 67 dollars do you think you would get major benefits from that?
Yeah I've reached much higher perf but
- it feels logarithmic (like most perf-compute graphs), and eventually plateaus. 44% @ 67 cents was a good stopping point for me
- more compute would require a lot of effort and dealing with new problems like training stability, cost of iterations/sweeps (didnt have the money to convincingly run larger iterations)
Thank you for answering these questions. Looking forward for the next write up about this.
Even cooler is his about me mention of saving his own life https://mvakde.github.io/ > Saved myself in a medical emergency (doctors didn't know what rhabdomyolysis was)
Crazy, considering rhabdo isn't that rare.
Everyone and their dog who is on statins knows what rhabdo is. Bonkers!
“What do you call a medical student who graduated at the bottom of their class?”
“Doctor.”
Well, I guess it would be nice if the graduation cutoff were above the level of “knows what rhabdo is”
> Also, I’m not sure whether “general reasoning” even exists in the first place? Maybe humans are specialised too
I have been wondering the same. We are now exposed to so many stimuli, we are tricked into thinking this is the norm - to have a reasonable understanding about everything, unless specialization is called for.
Really impressive and creative research. I wonder if the leading labs do anything similar with their models? It doenst look like the open source labs do?
How does it perform on ARC-AGI-3?
There was this a few weeks ago:
"Schema Harness Achieves ~99% on Arc‑AGI‑3 Public" https://news.ycombinator.com/item?id=48938163
>> Schema, the harness we introduce today, reaches 99% on the ARC_AGI_3 Public set using Claude Opus 4.8 and Fable 5, and 95.35% using GPT‑5.6 Sol
What does that do with 5.6 Luna instead of the expensive models?
What of 'schema' would improve the performance of mdlARC?
mdlARC: https://github.com/mvakde/mdlARC
There's an updated ARC-AGI-1 chart with 5.6 Luna in each thinking level in this video from last week: "A New Architecture [..] | MOONSHOTS " https://youtube.com/watch?v=qQfUbo7Ldc0&t=2m5s
its not gonna do well on ARC-3 without some significant changes and effort
The new arch in that video is kinda misleading. Didn't really compare against proper baselines
> Increases in LLM scores are now mainly driven by post training (evidence in next section) and are probably a function of amount of synthetic data. They are learning to solve ARC tasks, not learn general abstract reasoning
Agreed and that's for any benchmark. Private tests are better but you still have to trust the provider to not log and use them for training.
That's why I like when a new set of tests like a new ARC-AGI version is published, that's where you can see which of the models abstracted to more general capabilities instead of being focused on the previous tasks. Most models completely fail new ARC-AGI tests.
The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results. You hit a ceiling very fast and investing into more compute will give you diminishing results. So yes, you can train a custom model to do somewhat decently on a specific set of tasks but then what?
Then nothing - that's awesome. People think that LLMs are the know-all do-all solution to every problem now.
Putting solutions in terms of cents is a great way to potentially win over some ai boosters imo. There are other ways to solve hard problems.
> The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results.
I think thats unfair. Perf-compute is often logarithmic and will always saturate . Reaching the plateau faster is valuable as it often leads to better peaks (held true here and also look at modded nanogpt)
And more compute increases the perf (after dealing with other scaling problems)
Is the author only running their model against one benchmark? I don't think anyone finds that difficult to achieve, the difficulty comes when you want to make the model not benchmaxxed to a specific benchmark, and generalize so it can solve problems not part of the training data, but seems this model is specifically for not this? How useful is that?
If you just wanted to pass these specific tasks in this specific benchmark, and wanted to do so cheaply, I'm sure a non-LLM-based approach would yield better results for even cheaper, since what the author's model does, seem to basically be "solve ARC puzzles", not a general LLM or "coding" LLM.
I read this as a response to the current hype around LLMs. He is showing computers can solve these issues, without using an LLM architecture. A lot of people have sort of forgot that machine learning is more than just LLMs these days.
I found it to be a very interesting angle.
> He is showing computers can solve these issues, without using an LLM architecture.
Isn't it a LLM he's building though? My very point is that this particular use case could be solved better without building a LLM, now you claim he is not? The description of what he's doing surely makes it sound like it's a (very small) LLM, and personally I'm still on the "if it quacks like a duck" train in life.
> A lot of people have sort of forgot that machine learning is more than just LLMs these days.
Yeah, which I guess if you make my previous comment more concise, is exactly what I state too.
Nowhere does he say he built an llm. Hes using a transformer, not an llm.
> Nowhere does he say he built an llm. Hes using a transformer, not an llm.
Please describe what in your mind a "LLM" is exactly, then describe what this person is building. To me this sounds like "He's not building a calculator, he's just building a program that can do addition, minus, multiplication and division and display the results".
Obviously it's not a Large Language Model, but to me this looks more like a LLM than not, given the architecture he's chosen. But again, maybe I misunderstand?
Its not an LLM if there's no pretraining. AR transformers were around before LLMs and will be there after LLMs.
When I made this, the point was to show that you dont need pretraining (which is what makes an LLM) to perform well on complex tasks
And yes it is not a language model either. I did not train it on any language data. Only ARC puzzles
Out of interest, would you call BERT an LLM? It’s pre trained but not particularly large.
idk the definition is fuzzy. thats why people use the "modern" qualifier to talk about decoder-only style and this is also not clean since you now have reasoning models which are separate
AFAIK, the “large” qualifier came when transformers allowed to scale the size of language models compared to the recurrent models that where in fashion before. And although BERT isn't large by today's standard, it was large enough for the time.
A LLM should at the very least be a language model, i.e. be able to take human-readable text as input or produce it as output. Transformers are used for plenty of tasks that don't involve language, for example object detection or blind source separation, where the models aren't called LMs; and on the other hand there are some LLM architectures that exclusively use linear attention variants and aren't really transformers anymore.
It’s neither large nor language-based. ARC-AGI-1 is grid-based and nonverbal.
Use of a transformer is not necessary or sufficient to qualify as an LLM.
The whole point of his model is to optimize for a very specific benchmark.
BUT, he does not use labels when training, so the model does not know the answers.
> The whole point of his model is to optimize for a very specific benchmark.
But benchmaxxing is what we generally try to avoid for training, as there is no point really for it. We used to call it "overfitting", now you're saying this person does it intentionally? Why?
There are plenty of applications where a machine learning system needs to optimize for a very limited data set that is still intractable by linear logic systems of reasonable scale and complexity. It’s interesting, because he is using the legos of LLMs to build highly specialized machine learning systems, which is a very pragmatic approach. Obviously a lot of other ways to achieve similar goals, but it’s cool to see someone back porting the modern tools towards older style optimizations.
Also, the complexity of the task he is using occupies an interesting middle ground of ultra high dimensionality (for a “simple” problem) while being limited in width to a narrow set of solves- a space where one would be tempted to imagine you would need a much more capable system.
Why not? There is $700k reward for the next iteration of this benchmark https://www.kaggle.com/competitions/arc-prize-2026-arc-agi-2...
I would not call this overfitting, it's finetuning for specific task where you have a benchmark.
[dead]
"I don’t understand why others didn’t figure this out"
- how about we allot the possibility that so many of presumed ML experts don't have any clue what they be doing, and are eventually API bitches, nothing more.