There is some base level of intelligence any model needs to be useful, even in narrow tasks.
Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge...
Small models need to have enough base knowledge to be able to be good enough -- even in a seemingly narrow regime. Where is that? Obviously they don't need all the obscure knowledge of a frontier model but there is some base level which is probably more than it would first seem.
> Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge...
I would be interested to see a formal study of this. I say this not out of anything other than a observation that I think the only real blockers are a) judgement, and b) physical reflexes/strength. As a kid I was certainly aware of ice,snow, and rain, because I road my bike year round and had low confidence in my own ability to control my bike on snowy or wet terrain, especially during season changes. That translated into learning to drive in northern Canada in the winter and applying those lessons to driving.
In an environment devoid of consequences, I have seen kids operate driving simulations (both real simulations, and video games) with a degree of precision that is shocking, including seeing several 9-11 year olds play the simulations and games with a much higher degree of confidence than adult drivers. Children have an awareness that the simulations are consequence free, unless given other motivation. Adults that are consistent drivers have muscle memory and preconceived expectations that govern the decisions they make when playing the game. I am curious about the level of training and exposure required for children to overcome their lack of awareness of the hard limits and consequences of driving and driver error, versus the amount of training and exposure required for expert drivers that are novice gamers to stop applying their learned experience to consequence free simulations.
A 10 year old definitely,and 5year old is close, but not unrealistic, To drive a car you don't need to be able to read... To drive a car on the road with other people is a whole other story :-)
I suspect plenty of five year olds can do a respectable job in Mario Kart, Gran Turismo, etc driving games. Gaming has too low of stakes to judge them on perfectly adhering to the rules of the road, but the ability is there.
Being able to drive a car properly also depends on having the right exploration-exploitation balance. A three-year-old is likely to explore too much in a situation where mistakes can be dangerous.
This requires not only knowledge, but also the control systems that develop with the prefrontal cortex. LLMs don't do much control yet.
While I agree with your assessment, probably could've chosen a better example, as in many countries young kids even as young as 8 will learn how to drive.
This is more of a question of the definition of "drive a car" than any specific issue about intelligence. Drive a car without errors? Impossible, and now we're into a subjective discussion about what feels intelligent. Pass the DMV test? Probably. How complicated are the conditions? There are plenty of drivers with bad judgement. It's a quicksand sort of discussion.
Lots of confusion about what this model is actually focused on.
It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar.
"Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check.
So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.
Am I right in thinking this is a tiny model which has been trained well to reason, and that's it? Makes me think of a smart person who doesn't know anything about a given topic, but with the right tools will go and research the heck out of it. I really like the sound of this... why have models train on learning anything when you can just train them how to learn and let them get on with it from something as small as a Pi Zero and an internet connection.
This has been my dream ever since. Instead of encoding "all the knowledge" into those parameters, how about just making a model that has the same size, but all (or rather most) it does is reasoning? Just give it the ability to browse the net (e.g. language specifications, documentation and best practices) and just have it do its thing. Why does my coding agent need to know the population of New York, know a cheese cake recipe or the general lifespan of an ostrich? Just give it the bare minimum knowledge to think and reason about, and let it figure out the rest.
Sadly that's not how LLMs work, since all they do is "token prediction". At least the models we have to today ...
It would also reduce training costs to nothing. Current methodology requires continual retraining to scoop up new facts. If you can do a one time "this is how to think" - that could conceptually work forever, just plug in a new database layer that can be queried as required.
I think you could probably train a model to consider boolean logic, modal logic, and mathematics reasonably well, but there is still a pretty big leap between that and thinking about things.
Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box.
Requires knowledge of things not mentioned in the question (notably gravity).
Strict definition of all terms quickly gets you into a quagmire of complexity. Some base level of knowledge about things is required for you to give it instructions. If it only knows how to reason, it lacks any idea of what to aim to achieve.
There is quite a pronounced disconnect between the vast stores of written data that models are trained on and robust consideration of a topic. I do wonder if the path can be directed by the order of training.
For example if you train a model to basic literacy using tinystories, then math and philosopy texts, then psychology, and sociology texts, and then finally the mass data of everything from conversations and rants, to code and fiction.
Does that end up with a significantly different model to one that is trained on books on acting, creative writing, and fantasy novels, before introducing the same final mass data set.
How much does it's current ability allow it to contextualise new training data?
Yeah but don't you think like that's an oversimplication with the metaphor if we assume this model can do a smart human-level analysis and distillation of knowledge, no? I mean if that were true (i.e. its just like that) then yeah there is no need for massive models but I really would doubt that.
Even recent massive models do not work anything like a smart human does at the moment so why are we assuming this can?
The interesting thing about models this small is they should be able to be put on a single Taalas chip (the HC1 already runs a Llama 3.1 8B model). We're already at the point where half-decent reasoning could be run on an ASIC (and at mind-boggling speeds).
Having some success while testing this model out as a replacement for GPT-5 nano in source code security review. Running on RTX 3090 (24 GB VRAM) via vLLM. It's not great on structured output (as noted in the model card) but I'm working around that in my harness.
Does python coding depend on political facts of the world?
It might appear not, but actually, the process of reasoning is not an isolated act. The right and wrong way of doing things is codified in social evolution that absorbed all facets of life. Why should you optimize a piece of code for performance? Why performance is needed? What is a bug? What features and UI themes would be more intuitive for humans?
There is a butterfly effect. Everything affects everything to some extent.
I think this is predicted? Part of the story is how they were able to preserve core reasoning ability while cutting knowledge like "pelicans have wings."
> these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios.
This model doesn't support tool calling, was not part of its training. It's focused on Python (and I think C++) competitive programming and mathematics tasks, i.e. tasks with verifiable rewards. So if you have a task that fits that description, the size-to-capability ratio is good.
These kinds of models might be more useful as tools to be used by larger orchestrator models, than being the orchestrators themselves.
I'm not seeing any mention of tools in the paper, much less a bias towards "curiosity" to use those tools when it encounters gaps in its knowledge. So perhaps this is a good proof-of-concept that single-pass code generation is viable with this small a model - but we're still a long way from a viable solution.
try it again but give a careful explanation of what a bicycle and a pelican is and how the pelican would sit atop the bicycle. Then give it a reference to the SVG tags you want it to use with documentation.
Imagine you want to make a smaller model that is really good at one thing, say, driving a car. You could remove the parameters that lead it to correctly answer, "What is the powerhouse of the cell?" or, "Who was the first president of the United States?"
It would look really dumb if someone asked it that, but that's fine. You're trying to make a model that is optimized for efficiency for a specific task. As much as possible, you should prune uncorrelated things.
In this case, I’d expect it should make a web search tool call to find the Python library best suited for SVG generation and manipulation, and then use what it learns there to execute the task you’ve asked it to do (either asking if you’d like to incorporate the library as a dependency or to roll its own implementation of a subset of the features if that was your preference),
Assuming tool calling hasn’t been entirely stripped out of this model.
It's terrible at hunting security bugs (I expected it to be, but I wanted to be sure). I added it to a benchmark I made with a corpus of some Mythos-discovered bugs, and it found zero. The smallest pretty successful models remain Qwen 3.6 and Gemma 4 (but I haven't tested the very small variants of those yet).
The lack of tool use will hinder it a lot I think, since bug hunting requires collecting context across a code base and stitching it together. It might be good in a more narrow sense, i.e "is there a bug in this block of code" and not considering how it interacts with the rest of the code base.
That's also more aligned to its leetcode style training data, the code under test is fully in the context window. It might be interesting to have a bigger tool use model go through the effort of collecting the context, and feeding it into this kind of model for analysis only. It becomes more of a thinking tool, instead of the orchestrator.
Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge...
Small models need to have enough base knowledge to be able to be good enough -- even in a seemingly narrow regime. Where is that? Obviously they don't need all the obscure knowledge of a frontier model but there is some base level which is probably more than it would first seem.
Emphatically, it does not. Passing your drivers test may require being able to read, but plenty of illiterate people around the world drive just fine.
There is a reason we made all the common road signs recognisable purely by shape/colour, after all.
I would be interested to see a formal study of this. I say this not out of anything other than a observation that I think the only real blockers are a) judgement, and b) physical reflexes/strength. As a kid I was certainly aware of ice,snow, and rain, because I road my bike year round and had low confidence in my own ability to control my bike on snowy or wet terrain, especially during season changes. That translated into learning to drive in northern Canada in the winter and applying those lessons to driving.
In an environment devoid of consequences, I have seen kids operate driving simulations (both real simulations, and video games) with a degree of precision that is shocking, including seeing several 9-11 year olds play the simulations and games with a much higher degree of confidence than adult drivers. Children have an awareness that the simulations are consequence free, unless given other motivation. Adults that are consistent drivers have muscle memory and preconceived expectations that govern the decisions they make when playing the game. I am curious about the level of training and exposure required for children to overcome their lack of awareness of the hard limits and consequences of driving and driver error, versus the amount of training and exposure required for expert drivers that are novice gamers to stop applying their learned experience to consequence free simulations.
https://www.youtube.com/watch?v=BWAK0J8Uhzk
This requires not only knowledge, but also the control systems that develop with the prefrontal cortex. LLMs don't do much control yet.
I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.
It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar.
"Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check.
So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.
Sadly that's not how LLMs work, since all they do is "token prediction". At least the models we have to today ...
Turns out that without the world knowledge to have a base of facts, it is not.
Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box.
Requires knowledge of things not mentioned in the question (notably gravity).
Strict definition of all terms quickly gets you into a quagmire of complexity. Some base level of knowledge about things is required for you to give it instructions. If it only knows how to reason, it lacks any idea of what to aim to achieve.
There is quite a pronounced disconnect between the vast stores of written data that models are trained on and robust consideration of a topic. I do wonder if the path can be directed by the order of training.
For example if you train a model to basic literacy using tinystories, then math and philosopy texts, then psychology, and sociology texts, and then finally the mass data of everything from conversations and rants, to code and fiction.
Does that end up with a significantly different model to one that is trained on books on acting, creative writing, and fantasy novels, before introducing the same final mass data set.
How much does it's current ability allow it to contextualise new training data?
Even recent massive models do not work anything like a smart human does at the moment so why are we assuming this can?
How?
It might appear not, but actually, the process of reasoning is not an isolated act. The right and wrong way of doing things is codified in social evolution that absorbed all facets of life. Why should you optimize a piece of code for performance? Why performance is needed? What is a bug? What features and UI themes would be more intuitive for humans?
There is a butterfly effect. Everything affects everything to some extent.
> these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios.
These kinds of models might be more useful as tools to be used by larger orchestrator models, than being the orchestrators themselves.
It would look really dumb if someone asked it that, but that's fine. You're trying to make a model that is optimized for efficiency for a specific task. As much as possible, you should prune uncorrelated things.
Assuming tool calling hasn’t been entirely stripped out of this model.
(Edit) No tool calling, per this comment: https://news.ycombinator.com/item?id=48640189
https://swelljoe.com/post/will-it-mythos/
That's also more aligned to its leetcode style training data, the code under test is fully in the context window. It might be interesting to have a bigger tool use model go through the effort of collecting the context, and feeding it into this kind of model for analysis only. It becomes more of a thinking tool, instead of the orchestrator.