Based on a quick first skim of the abstract and the introduction, the results from hierarchical reasoning (HRM) models look incredible:
> Using only 1,000 input-output examples, without pre-training or CoT supervision, HRM learns to solve problems that are intractable for even the most advanced LLMs. For example, it achieves near-perfect accuracy in complex Sudoku puzzles (Sudoku-Extreme Full) and optimal pathfinding in 30x30 mazes, where state-of-the-art CoT methods completely fail (0% accuracy). In the Abstraction and Reasoning Corpus (ARC) AGI Challenge 27,28,29 - a benchmark of inductive reasoning - HRM, trained from scratch with only the official dataset (~1000 examples), with only 27M parameters and a 30x30 grid context (900 tokens), achieves a performance
of 40.3%, which substantially surpasses leading CoT-based models like o3-mini-high (34.5%) and Claude 3.7 8K context (21.2%), despite their considerably larger parameter sizes and context lengths, as shown in Figure 1.
I'm going to read this carefully, in its entirety.
Thank you for sharing it on HN!
lumost · 2h ago
I am extremely skeptical of a 27M parameter model being trained “from scratch” on 1000 datapoints. I am likewise incredulous of the lack of comparison with any other model which is trained “from scratch” using their data preparation. Instead they strictly compare with 3rd party LLMs which are massively more general purpose and may not have any of those 1000 examples in their training set.
This smells like some kind of overfit to me.
diwank · 13h ago
Exactly!
> It uses two interdependent recurrent modules: a *high-level module* for abstract, slow planning and a *low-level module* for rapid, detailed computations. This structure enables HRM to achieve significant computational depth while maintaining training stability and efficiency, even with minimal parameters (27 million) and small datasets (~1,000 examples).
> HRM outperforms state-of-the-art CoT models on challenging benchmarks like Sudoku-Extreme, Maze-Hard, and the Abstraction and Reasoning Corpus (ARC-AGI), where CoT methods fail entirely. For instance, it solves 96% of Sudoku puzzles and achieves 40.3% accuracy on ARC-AGI-2, surpassing larger models like Claude 3.7 and DeepSeek R1.
Erm what? How? Needs a computer and sitting down.
cs702 · 9h ago
Yeah, that was pretty much my reaction. I will need time on a computer too.
I love it when authors publish working code. It's usually a good sign. If the code does what the authors claim, no one can argue with it!
diwank · 8h ago
Same! Guan’s work on sample packing during finetuning has become a staple. His openchat code is also super simple and easy to understand.
mkagenius · 11h ago
Is it talking about fine tuning existing models with 1000 examples to beat them in those tasks?
nowittyusername · 17m ago
I've been keeping an eye on this one as well. based on what the paper claims this would be huge. But i think like many here, we are waiting for either confirmation or denial of the claim via 3d parties. the concept behind them sounds legit, but id like to see it in practice.
SubiculumCode · 5h ago
As a cognitive psychologist, I highly suspected that, broadly speaking, this was the needed direction for AI. See Fuzzy Trace Theory[1].
Fuzzy Trace Theory basically suggests that memory (and cognition generally) works at multiple levels spanning verbatim representations to gist-level representations, that get bound together into memories. Recalling gist, the general idea, along with specific details, allows for powerful generalization and flexible retrieval pathways.
> "After completing the T steps, the H-module incorporates the sub-computation’s outcome (the final state L) and performs its own update. This H update establishes a fresh context for the L-module, essentially “restarting” its computational path and initiating a new convergence phase toward a different local equilibrium."
So they let the low-level RNN bottom out, evaluate the output in the high level module, and generate a new context for the low-level RNN. Rinse, repeat. The low-level RNNs are iterating backpropagation while the high-level is periodically kicking the low-level RNNs to get better outputs. Loops within loops. Composition.
Another interesting part:
> "Neuroscientific evidence shows that these cognitive modes share overlapping neural circuits, particularly within regions such as the prefrontal cortex and the default mode network. This indicates that the brain dynamically modulates the “runtime” of these circuits according to task complexity and potential rewards.
> Inspired by the above mechanism, we incorporate an adaptive halting strategy into HRM that enables `thinking, fast and slow'"
A scheduler that dynamically balances resources based on the necessary depth of reasoning and the available data.
I love how this paper cites parallels with real brains throughout. I believe AGI will be solved as the primitives we're developing are composed to extreme complexity, utilizing many cooperating, competing, communicating, concurrent, specialized "modules." It is apparent to me that human brain must have this complexity, because it's the only feasible way evolution had to achieve cognition using slow, low power tissue.
username135 · 9h ago
As soon I read the hlm/llm split, it immediately reminded me of the human brain.
esafak · 5h ago
Composition is the whole point of deep learning. Deep as in multilayer, multilevel.
dbagr · 4h ago
You need recursion at some point: you can't account for all possible scenarios of combinations, as you would need an infinite number of layers.
crystal_revenge · 3h ago
> infinite number of layers
That’s not as impossible as it seems, Gaussian Processes are equivalent to a Neural Network with infinite hidden units, and any multilayer NN can be approximated by one with a single, larger layer of hidden units.
advael · 2h ago
I mean recurrence is an attempt to allow approximation of recursive processes, no?
JonathanRaines · 10h ago
I advise scepticism.
This work does have some very interesting ideas, specifically avoiding the costs of backpropagation through time.
However, it does not appear to have been peer reviewed.
The results section is odd. It does not include include details of how they performed the assesments, and the only numerical values are in the figure on the front page. The results for ARC2 are (contrary to that figure) not top of the leaderboard (currently 19% compared to HRMs 5% https://www.kaggle.com/competitions/arc-prize-2025/leaderboa...)
In fields like AI/ML, I'll take a preprint with working code over peer-reviewed work without any code, always, even when the preprint isn't well edited.
Everyone everywhere can review a preprint and its published code, instead of a tiny number of hand-chosen reviewers who are often overworked, underpaid, and on tight schedules.
If the authors' claims hold up, the work will gain recognition. If the claims don't hold up, the work will eventually be ignored. Credentials are basically irrelevant.
Think of it as open-source, distributed, global review. It may be messy and ad-hoc, since no one is in charge, but it works much better than traditional peer review!
smokel · 8h ago
I sympathize partially with your views, but how would this work in practice? Where would the review comments be stored? Is one supposed to browse Hacker News to check the validity of a paper?
If a professional reviewer spots a serious problem, the paper will not make it to a conference or journal, saving us a lot of trouble.
yorwba · 6h ago
Peer review is a way to distribute the work of identifying which papers are potentially worth reading. If you're starting from an individual paper and then ask yourself whether it was peer reviewed or not, you're doing it wrong. If you really need to know, read it yourself and accept that you might just be wasting your time.
If you want to mostly read papers that have already been reviewed, start with people or organizations you trust to review papers in an area you're interested in and read what they recommend. That could be on a personal blog or through publishing a traditional journal, the difference doesn't matter much.
conception · 5h ago
“Find papers that support what you want via online echo chambers” isn’t the advice you want to be giving but it is the net result of it. Society needs trusted institutions. Not that publishers are the best result of that but adhoc blog posts are decidedly not better.
yorwba · 4h ago
It is totally the advice I want to be giving. Given the choice between an echo chamber matched to my interests and wading through a stream of unfiltered crap, I'll take the echo chamber every time. (Of course there's also the option of not reading papers at all, which is typically a good choice if you're not a subject matter expert and don't intend to put in the work to become one.)
If you choose to focus on the output of a well-known publisher, you're not avoiding echo chambers, you're using a heuristic to hopefully identify a good one.
ricardobeat · 3h ago
Those are not the only options, namely the parent mentioned 'trusted institutions'. It is the best way to defer that filtering to a group of other humans, whose collective expertise will surpass any one individual.
The destruction of trust in both public and private institutions - newspapers, journals, research institutions, universities - and replacement with social media 'influencers' and online echo chambers is how we arrived at the current chaotic state of politics worldwide, the rise of extremist groups, cults, a resurgence of nationalism, religious fanaticism... This is terrible advice.
belter · 7h ago
> If a professional reviewer spots a serious problem
Did that ever happen? :-)
atq2119 · 7h ago
Of course. As usual, you tend to not hear about it when a system we rely on works well.
hodgehog11 · 8h ago
Scepticism is generally always a good idea with ML papers. Once you start publishing regularly in ML conferences, you understand that there is no traditional form of peer review anymore in this domain. The volume of papers has meant that 'peers' are often students coming to grips with parts of the field that rarely align with what they are asked to review. Conference peer review has become a 'vibe check' more than anything.
Real peer review is when other experts independently verify your claims in the arXiv submission through implementation and (hopefully) cite you in their followup work. This thread is real peer review.
dleeftink · 7h ago
I appreciate this insight, makes you wonder, why even publish a paper if it only amounts to a vibe check? If it's just the code we need we can get that peer reviewed through other channels.
thfuran · 7h ago
Because publications is the number that academics have to make go up.
hodgehog11 · 7h ago
This and the exposure. There are so many papers on arXiv now that people often look to conference or journal publication lists.
dleeftink · 5h ago
The number has clearly ceased its function, so what are we chasing?
gavinray · 4h ago
Clout, funding, and employment I'd imagine?
rapatel0 · 7h ago
THIS is so true but also not limited to ML.
Having been both a publisher and reviewer across multiple engineering, science, and bio-medical disciplines this occurs across academia.
d4rkn0d3z · 10h ago
Skepticism is best expressed by repeating the experiment and comparing results. I'm game and I have 10 days off work next month. I wonder what can be had in terms of full source and data, etc. from the authors?
Nice! They provide trained checkpoints on their GitHub. Repeating their results would be a good start.
https://github.com/sapientinc/HRM
diwank · 8h ago
I think that’s too harsh a position solely for not being peer reviewed yet. Neither of yhe original mamba1 and mamba2 papers were peer reviewed. That said, strong claims warrant strong proofs, and I’m also trying to reproduce the results locally.
mitthrowaway2 · 2h ago
Do you consider yourself a peer? Feel free to review it.
A peer reviewer will typically comment that some figures are unclear, that a few relevant prior works have gone uncited, or point out a followup experiment that they should do.
That's about the extent of what peer reviewers do, and basically what you did yourself.
riku_iki · 5h ago
> However, it does not appear to have been peer reviewed.
my observation is that peer reviewers never try to reproduce results or do basic code audit to check that there is no data leak for example to training dataset.
frozenseven · 6h ago
>does not appear to have been peer reviewed
Enough already. Please. The paper + code is here for everybody to read and test. Either it works or it doesn't. Either people will build upon it or they won't. I don't need to wait 20 months for 3 anonymous dudes to figure it out.
sigmoid10 · 8h ago
Skepticism is an understatement. There are tons of issues with this paper. Why are they comparing results of their expert model that was trained from scratch on a single task to general purpose reasoning models? It is well established in the literature that you can still beat general purpose LLMs in narrow domain tasks with specially trained, small models. The only comparison that would have made sense is one to vanilla transformers using the same nr of parameters and trained on the same input-output dataset. But the paper shows no such comparison. In fact, I would be surprised if it was significantly better, because such architecture improvements are usually very modest or not applicable in general. And insinuating that this is some significant development to improve general purpose AI by throwing in ARC is just straight up dishonest. I could probably cook up a neural net in pytorch in a few minutes that beats a hand-crafted single task that o3 can't solve in an hour. That doesn't mean that I made any progress towards AGI.
bubblyworld · 8h ago
Have you spent much time with the ARC-1 challenge? Their results on that are extremely compelling, showing results close to the initial competition's SOTA (as of closing anyway) with a tiny model and no hacks like data augmentation, pretraining, etc that all of the winning approaches leaned on heavily.
Your criticism makes sense for the maze solving and sudoku sets, of course, but I think it kinda misses the point (there are traditional algos that solve those just fine - it's more about the ability of neural nets to figure them out during training, and known issues with existing recurrent architectures).
Assuming this isn't fake news lol.
smokel · 8h ago
Looking at the code, there is a lot of data augmentation going on there. For the Sudoku and ARC data sets, they augment every example by a factor of 1,000.
That's fair, they are relabelling colours and rotating the boards. I meant more like mass generation of novel puzzles to try and train specific patterns. But you are right that technically there is some augmentation going on here, my bad.
smokel · 8h ago
Hm, I'm not so sure it's fair play for the Sudoku puzzle. Suggesting that the AI will understand the rules of the game with only 1,000 examples, and then adding 1,000,000 derived examples does not feel fair to me. Those extra examples leak a lot of information about the rules of the game.
I'm not too familiar with the ARC data set, so I can't comment on that.
bubblyworld · 8h ago
True, it leaks information about all the symmetries of the puzzle, but that's about it. I guess someone needs to test how much that actually helps - if I get the model running I'll give it a try!
westurner · 5h ago
> That's fair, they are relabelling colours and rotating the boards.
Photometric augmentation, Geometric augmentation
> I meant more like mass generation of novel puzzles to try and train specific patterns.
What is the difference between Synthetic Data Generation and Self Play (like AlphaZero)? Don't self play simulations generate synthetic training data as compared to real observations?
bubblyworld · 4h ago
I don't know the jargon, but for me the main thing is the distinction between humans injecting additional bits of information into the training set vs the algorithm itself discovering those bits of information. So self-play is very interesting (it's automated as part of the algorithm) but stuff like generating tons of novel sudoku puzzles and adding them to the training set is less interesting (the information is being fed into the training set "out-of-band", so to speak).
In this case I was wrong, the authors are clearly adding bits of information themselves by augmenting the dataset with symmetries (I propose "symmetry augmentation" as a much more sensible phrase for this =P). Since symmetries share a lot of mutual information with each other, I don't think this is nearly as much of a crutch as adding novel data points into the mix before training, but ideally no augmentation would be needed.
I guess you could argue that in some sense it's fair play - when humans are told the rules of sudoku the symmetry is implicit, but here the AI is only really "aware" of the gradient.
sigmoid10 · 8h ago
As the other commenter already pointed out, I'll believe it when I see it on the leaderboard. But even then it already lost twice against the winner of last year's competition, because that too was a general purpose LLM that could also do other things.
bubblyworld · 8h ago
Let's not move the goalposts here =) I don't think it's really fair to compare them directly like that. But I agree, this is triggering my "too good to be true" reflex very hard.
sigmoid10 · 8h ago
If anything, they moved the goalpost closer to the starting line. I'm merely putting it back where it belongs.
smokel · 9h ago
If I understand this correctly, it learns the rules of Sudoku by looking at 1,000 examples of (puzzle, solution) pairs. It is then able to solve previously unseen puzzles with 55% accuracy. If given millions of examples, it becomes almost perfect.
This is apparently without pretraining of any sort, which is kind of amazing. In contrast, systems like AlphaZero have the rules to go or chess built-in, and only learn the strategy, not the rules.
Off to their GitHub repository [1] to see this for myself.
AlphaZero may have the rules built in, but MuZero and the other follow-ups didn't. MuZero not only matched or surpassed AlphaZero, but it did so with less training, especially in the EfficientZero variant; notably also on the Atari playground.
smokel · 8h ago
Thanks for pointing that out.
To be fair, MuZero only learns a model of the rules for navigating its search tree. To make actual moves, it gets a list of valid actions from the game engine, so at that level it does not learn the rules of the game.
(HRM possibly does the same, and could be in the same realm as MuZero. It probably makes a lot of illegal moves.)
gavmor · 8h ago
This is "The Bitter Lesson" of AI, no? "More compute beats clever algorithm."
babel_ · 2h ago
Quite the opposite, a clever algorithm needs less compute, and can leverage extra compute even more.
gavmor · 1h ago
Apologies, "clever" is a poor paraphrase of "domain-specific", or "methods that leveraged human understanding."[0]
To follow up, after experimenting a bit with the source code:
1. Please, for the love of God, and for scientific reproducibility, specify library versions explicitly, and use pyproject.toml instead of an incomplete requirements.txt.
2. The 1,000 Sudoku examples are augmented with hand-coded permutation algorithms, so the actual input data set is more like 1,000,000 examples, not 1,000.
rudedogg · 7h ago
Do you have a fork or the changes? I might take a look, and python dependency hell on Sunday is no good
mkagenius · 4h ago
> specify library versions explicitly
Sometimes even that is not helpful. It's a pain we have to deal with.
gavinray · 4h ago
How is it not helpful?
A dependency lock file with resolved versions for both direct and transient dependencies = reproducible build
mkagenius · 3h ago
I don't remember the exact scenario but it might have been related to the underlying python or some sys library being a little different and then the dependency lock not being compatible with it.
lispitillo · 12h ago
I hope/fear this HRM model is going to be merged with MoE very soon. Given the huge economic pressure to develop powerful LLMs I think this can be done in just a month.
The paper seems to only study problems like sudoku solving, and not question answering or other applications of LLMs. Furthermore they omit a section for future applications or fusion with current LLMs.
I think anyone working in this field can envision their applications, but the details to have a MoE with an HRM model could be their next paper.
I only skimmed the paper and I am not an expert, sure other will/can explain why they don't discuss such a new structure. Anyway, my post is just blissful ignorance over the complexity involved and the impossible task to predict change.
Edit: A more general idea is that Mixture of Expert is related to cluster of concepts and now we would have to consider a cluster of concepts related by the time they take to be grasped, so in a sense the model would have in latent space an estimation of the depth, number of layers, and time required for each concept, just like we adapt our reading style for a dense math book different to a newspaper short story.
yorwba · 11h ago
This HRM is essentially purpose-designed for solving puzzles with a small number of rules interacting in complex ways. Because the number of rules is small, a small model can learn them. Because the model is small, it can be run many times in a loop to resolve all interactions.
In contrast, language modeling requires storing a large number of arbitrary phrases and their relation to each other, so I don't think you could ever get away with a similarly small model. Fortunately, a comparatively small number of steps typically seems to be enough to get decent results.
But if you tried to use an LLM-sized model in an HRM-style loop, it would be dog slow, so I don't expect anyone to try it anytime soon. Certainly not within a month.
Maybe you could have a hybrid where an LLM has a smaller HRM bolted on to solve the occasional constraint-satisfaction task.
marcosdumay · 5h ago
> In contrast, language modeling requires storing a large number of arbitrary phrases and their relation to each other
A person has some ~10k word vocabulary, with words fitting specific places in a really small set of rules. All combined, we probably have something on the order of a few million rules in a language.
What, yes, is larger than the thing in this paper can handle. But is nowhere near as large as a problem that should require something the size of a modern LLM to handle. So it's well worth it to try to enlarge models with other architectures, try hybrid models (note that this one is necessarily hybrid already), and explore every other possibility out there.
energy123 · 10h ago
What about many small HRM models that solve conceptually distinct subtasks as determined and routed to by a master model who then analyzes and aggregates the outputs, with all of that learned during training.
buster · 12h ago
must say I am suspicious in this regard, as they don't show applications other than a Sudoku solver and don't discuss downsides.
Oras · 11h ago
and the training was only on Sudoku. Which means they need to train a small model for every problem that currently exists.
Back to ML models?
JBits · 2h ago
I would assuming that training a LLM would be unfeasible for a small research lab, so isn't tackling small problems like this unavoidable? Given that current LLMs have clear limitations, I can't think of anything better than developing beter architectures on small test cases, then a company can try scaling it later.
lispitillo · 10h ago
Not only on Sudoku, there is also maze solving and ARC-AGI.
malcontented · 4h ago
I appreciate the connections with neurology, and the paper itself doesn't ring any alarm bells. I don't think I'd reject it if it fell to me to peer review.
However, I have extreme skepticism when it comes to the applicability of this finding. Based on what they have written, they seem to have created a universal (maybe; adaptable at the very least) constraint-satisfaction solver that learns the rules of the constraint-satisfaction problem from a small number of examples. If true (I have not yet had the leisure to replicate their examples and try them on something else), this is pretty cool, but I do not understand the comparison with CoT models.
CoT models can, in principle, solve _any_ complex task. This needs to be trained to a specific puzzle which it can then solve: it makes no pretense to universality. It isn't even clear that it is meant to be capable of adapting to any given puzzle. I suspect this is not the case, just based on what I have read in the paper and on the indicative choice of examples they tested it against.
This is kind of like claiming that Stockfish is way smarter than current state of the art LLMs because it can beat the stuffing out of them in chess.
I feel the authors have a good idea here, but that they have marketed it a bit too... generously.
jurgenaut23 · 3h ago
Yes, I agree, but this is a huge deal in and of itself. I suppose the authors had to frame it in this way for obvious reasons of hype surfing, but this is an amazing achievement, especially given the small size of the model! I’d rather use a customized model for a specific problem than a supposedly « generally intelligent » model that burns orders of magnitude more energy for much less reliability.
JBits · 3h ago
> CoT models can, in principle, solve _any_ complex task.
What is the justification for this? Is there a mathematical proof?
To me, CoT seems like a hack to work around the severe limitations of current LLMs.
liamnorm · 2h ago
The Universal Approximation Theorem.
JBits · 1h ago
I don't see how that changes anything. By this logic, there's no need for CoT reasoning at all, as a single pass should be sufficient. I don't see how that proves that CoT increases capabilities.
OgsyedIE · 11h ago
Skimming this, there is no reason why a MoE LLM system (whether autoregressive, diffusion, energy-based or mixed) couldn't be given a nested architecture that duplicates the layout of a HRM. Combining these in different ways should allow for some novel benchmarks around efficiency and quality, which will be interesting.
advael · 2h ago
I really like this usage of recurrent modules to augment attention-based models, and I think this is a really cool result and a fruitful avenue for future work
camphy · 2h ago
This is really interesting, but does anyone think this is something that might generalize for ambiguous reasoning situations with more development? I am no expert, but sudoku and puzzles seem like very well-defined problem spaces.
0x000xca0xfe · 11h ago
Goodbye captchas I guess? Somehow they are still around.
belter · 7h ago
Is this not a variation of ReAct + Chain-of-Thought + Structured Planning? Or is that too unfair to the authors work?
> Using only 1,000 input-output examples, without pre-training or CoT supervision, HRM learns to solve problems that are intractable for even the most advanced LLMs. For example, it achieves near-perfect accuracy in complex Sudoku puzzles (Sudoku-Extreme Full) and optimal pathfinding in 30x30 mazes, where state-of-the-art CoT methods completely fail (0% accuracy). In the Abstraction and Reasoning Corpus (ARC) AGI Challenge 27,28,29 - a benchmark of inductive reasoning - HRM, trained from scratch with only the official dataset (~1000 examples), with only 27M parameters and a 30x30 grid context (900 tokens), achieves a performance of 40.3%, which substantially surpasses leading CoT-based models like o3-mini-high (34.5%) and Claude 3.7 8K context (21.2%), despite their considerably larger parameter sizes and context lengths, as shown in Figure 1.
I'm going to read this carefully, in its entirety.
Thank you for sharing it on HN!
This smells like some kind of overfit to me.
> It uses two interdependent recurrent modules: a *high-level module* for abstract, slow planning and a *low-level module* for rapid, detailed computations. This structure enables HRM to achieve significant computational depth while maintaining training stability and efficiency, even with minimal parameters (27 million) and small datasets (~1,000 examples).
> HRM outperforms state-of-the-art CoT models on challenging benchmarks like Sudoku-Extreme, Maze-Hard, and the Abstraction and Reasoning Corpus (ARC-AGI), where CoT methods fail entirely. For instance, it solves 96% of Sudoku puzzles and achieves 40.3% accuracy on ARC-AGI-2, surpassing larger models like Claude 3.7 and DeepSeek R1.
Erm what? How? Needs a computer and sitting down.
The repo is at https://github.com/sapientinc/HRM .
I love it when authors publish working code. It's usually a good sign. If the code does what the authors claim, no one can argue with it!
Fuzzy Trace Theory basically suggests that memory (and cognition generally) works at multiple levels spanning verbatim representations to gist-level representations, that get bound together into memories. Recalling gist, the general idea, along with specific details, allows for powerful generalization and flexible retrieval pathways.
[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4979567/
So they let the low-level RNN bottom out, evaluate the output in the high level module, and generate a new context for the low-level RNN. Rinse, repeat. The low-level RNNs are iterating backpropagation while the high-level is periodically kicking the low-level RNNs to get better outputs. Loops within loops. Composition.
Another interesting part:
> "Neuroscientific evidence shows that these cognitive modes share overlapping neural circuits, particularly within regions such as the prefrontal cortex and the default mode network. This indicates that the brain dynamically modulates the “runtime” of these circuits according to task complexity and potential rewards.
> Inspired by the above mechanism, we incorporate an adaptive halting strategy into HRM that enables `thinking, fast and slow'"
A scheduler that dynamically balances resources based on the necessary depth of reasoning and the available data.
I love how this paper cites parallels with real brains throughout. I believe AGI will be solved as the primitives we're developing are composed to extreme complexity, utilizing many cooperating, competing, communicating, concurrent, specialized "modules." It is apparent to me that human brain must have this complexity, because it's the only feasible way evolution had to achieve cognition using slow, low power tissue.
That’s not as impossible as it seems, Gaussian Processes are equivalent to a Neural Network with infinite hidden units, and any multilayer NN can be approximated by one with a single, larger layer of hidden units.
This work does have some very interesting ideas, specifically avoiding the costs of backpropagation through time.
However, it does not appear to have been peer reviewed.
The results section is odd. It does not include include details of how they performed the assesments, and the only numerical values are in the figure on the front page. The results for ARC2 are (contrary to that figure) not top of the leaderboard (currently 19% compared to HRMs 5% https://www.kaggle.com/competitions/arc-prize-2025/leaderboa...)
In fields like AI/ML, I'll take a preprint with working code over peer-reviewed work without any code, always, even when the preprint isn't well edited.
Everyone everywhere can review a preprint and its published code, instead of a tiny number of hand-chosen reviewers who are often overworked, underpaid, and on tight schedules.
If the authors' claims hold up, the work will gain recognition. If the claims don't hold up, the work will eventually be ignored. Credentials are basically irrelevant.
Think of it as open-source, distributed, global review. It may be messy and ad-hoc, since no one is in charge, but it works much better than traditional peer review!
If a professional reviewer spots a serious problem, the paper will not make it to a conference or journal, saving us a lot of trouble.
If you want to mostly read papers that have already been reviewed, start with people or organizations you trust to review papers in an area you're interested in and read what they recommend. That could be on a personal blog or through publishing a traditional journal, the difference doesn't matter much.
If you choose to focus on the output of a well-known publisher, you're not avoiding echo chambers, you're using a heuristic to hopefully identify a good one.
The destruction of trust in both public and private institutions - newspapers, journals, research institutions, universities - and replacement with social media 'influencers' and online echo chambers is how we arrived at the current chaotic state of politics worldwide, the rise of extremist groups, cults, a resurgence of nationalism, religious fanaticism... This is terrible advice.
Did that ever happen? :-)
Real peer review is when other experts independently verify your claims in the arXiv submission through implementation and (hopefully) cite you in their followup work. This thread is real peer review.
Having been both a publisher and reviewer across multiple engineering, science, and bio-medical disciplines this occurs across academia.
A peer reviewer will typically comment that some figures are unclear, that a few relevant prior works have gone uncited, or point out a followup experiment that they should do.
That's about the extent of what peer reviewers do, and basically what you did yourself.
my observation is that peer reviewers never try to reproduce results or do basic code audit to check that there is no data leak for example to training dataset.
Enough already. Please. The paper + code is here for everybody to read and test. Either it works or it doesn't. Either people will build upon it or they won't. I don't need to wait 20 months for 3 anonymous dudes to figure it out.
Your criticism makes sense for the maze solving and sudoku sets, of course, but I think it kinda misses the point (there are traditional algos that solve those just fine - it's more about the ability of neural nets to figure them out during training, and known issues with existing recurrent architectures).
Assuming this isn't fake news lol.
https://github.com/sapientinc/HRM/blob/main/dataset/build_ar...
I'm not too familiar with the ARC data set, so I can't comment on that.
Photometric augmentation, Geometric augmentation
> I meant more like mass generation of novel puzzles to try and train specific patterns.
What is the difference between Synthetic Data Generation and Self Play (like AlphaZero)? Don't self play simulations generate synthetic training data as compared to real observations?
In this case I was wrong, the authors are clearly adding bits of information themselves by augmenting the dataset with symmetries (I propose "symmetry augmentation" as a much more sensible phrase for this =P). Since symmetries share a lot of mutual information with each other, I don't think this is nearly as much of a crutch as adding novel data points into the mix before training, but ideally no augmentation would be needed.
I guess you could argue that in some sense it's fair play - when humans are told the rules of sudoku the symmetry is implicit, but here the AI is only really "aware" of the gradient.
This is apparently without pretraining of any sort, which is kind of amazing. In contrast, systems like AlphaZero have the rules to go or chess built-in, and only learn the strategy, not the rules.
Off to their GitHub repository [1] to see this for myself.
[1] https://github.com/sapientinc/HRM
To be fair, MuZero only learns a model of the rules for navigating its search tree. To make actual moves, it gets a list of valid actions from the game engine, so at that level it does not learn the rules of the game.
(HRM possibly does the same, and could be in the same realm as MuZero. It probably makes a lot of illegal moves.)
0. http://www.incompleteideas.net/IncIdeas/BitterLesson.html
1. Please, for the love of God, and for scientific reproducibility, specify library versions explicitly, and use pyproject.toml instead of an incomplete requirements.txt.
2. The 1,000 Sudoku examples are augmented with hand-coded permutation algorithms, so the actual input data set is more like 1,000,000 examples, not 1,000.
Sometimes even that is not helpful. It's a pain we have to deal with.
A dependency lock file with resolved versions for both direct and transient dependencies = reproducible build
The paper seems to only study problems like sudoku solving, and not question answering or other applications of LLMs. Furthermore they omit a section for future applications or fusion with current LLMs.
I think anyone working in this field can envision their applications, but the details to have a MoE with an HRM model could be their next paper.
I only skimmed the paper and I am not an expert, sure other will/can explain why they don't discuss such a new structure. Anyway, my post is just blissful ignorance over the complexity involved and the impossible task to predict change.
Edit: A more general idea is that Mixture of Expert is related to cluster of concepts and now we would have to consider a cluster of concepts related by the time they take to be grasped, so in a sense the model would have in latent space an estimation of the depth, number of layers, and time required for each concept, just like we adapt our reading style for a dense math book different to a newspaper short story.
In contrast, language modeling requires storing a large number of arbitrary phrases and their relation to each other, so I don't think you could ever get away with a similarly small model. Fortunately, a comparatively small number of steps typically seems to be enough to get decent results.
But if you tried to use an LLM-sized model in an HRM-style loop, it would be dog slow, so I don't expect anyone to try it anytime soon. Certainly not within a month.
Maybe you could have a hybrid where an LLM has a smaller HRM bolted on to solve the occasional constraint-satisfaction task.
A person has some ~10k word vocabulary, with words fitting specific places in a really small set of rules. All combined, we probably have something on the order of a few million rules in a language.
What, yes, is larger than the thing in this paper can handle. But is nowhere near as large as a problem that should require something the size of a modern LLM to handle. So it's well worth it to try to enlarge models with other architectures, try hybrid models (note that this one is necessarily hybrid already), and explore every other possibility out there.
Back to ML models?
However, I have extreme skepticism when it comes to the applicability of this finding. Based on what they have written, they seem to have created a universal (maybe; adaptable at the very least) constraint-satisfaction solver that learns the rules of the constraint-satisfaction problem from a small number of examples. If true (I have not yet had the leisure to replicate their examples and try them on something else), this is pretty cool, but I do not understand the comparison with CoT models.
CoT models can, in principle, solve _any_ complex task. This needs to be trained to a specific puzzle which it can then solve: it makes no pretense to universality. It isn't even clear that it is meant to be capable of adapting to any given puzzle. I suspect this is not the case, just based on what I have read in the paper and on the indicative choice of examples they tested it against.
This is kind of like claiming that Stockfish is way smarter than current state of the art LLMs because it can beat the stuffing out of them in chess.
I feel the authors have a good idea here, but that they have marketed it a bit too... generously.
What is the justification for this? Is there a mathematical proof? To me, CoT seems like a hack to work around the severe limitations of current LLMs.
[1] - https://arxiv.org/abs/2210.03629