[HN Gopher] Hierarchical Reasoning Model
___________________________________________________________________
Hierarchical Reasoning Model
Author : hansmayer
Score : 275 points
Date : 2025-07-27 07:15 UTC (15 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| torginus wrote:
| Is it just me or are symbolic (or as I like to call it 'video
| game') AI is seeping back into AI?
| taylorius wrote:
| Perhaps so - but represented in a trainable, neural form. Very
| exciting!
| cornholio wrote:
| Natural general intelligences sure seem to work this way.
| marcosdumay wrote:
| Training databases is much easier than training neural
| networks.
| bobosha wrote:
| But symbolic != hierarchical
| cs702 wrote:
| 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!
| diwank wrote:
| 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.
| mkagenius wrote:
| Is it talking about fine tuning existing models with 1000
| examples to beat them in those tasks?
| cs702 wrote:
| Yeah, that was pretty much my reaction. I will need time on a
| computer too.
|
| 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!
| diwank wrote:
| Same! Guan's work on sample packing during finetuning has
| become a staple. His openchat code is also super simple and
| easy to understand.
| lumost wrote:
| 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.
| electroglyph wrote:
| but does it scale?
| lispitillo wrote:
| 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.
| buster wrote:
| 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 wrote:
| 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?
| lispitillo wrote:
| Not only on Sudoku, there is also maze solving and ARC-AGI.
| JBits wrote:
| 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.
| yorwba wrote:
| 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.
| energy123 wrote:
| 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.
| marcosdumay wrote:
| > 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.
| OgsyedIE wrote:
| 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.
| 0x000xca0xfe wrote:
| Goodbye captchas I guess? Somehow they are still around.
| topspin wrote:
| > "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 wrote:
| As soon I read the hlm/llm split, it immediately reminded me of
| the human brain.
| esafak wrote:
| Composition is the whole point of deep learning. Deep as in
| multilayer, multilevel.
| dbagr wrote:
| 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 wrote:
| > 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 wrote:
| I mean recurrence is an attempt to allow approximation of
| recursive processes, no?
| JonathanRaines wrote:
| 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...)
| d4rkn0d3z wrote:
| 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?
| PJones2000 wrote:
| 24 hours on a 4070. Seems quite doable.
| https://github.com/sapientinc/HRM
| JonathanRaines wrote:
| Nice! They provide trained checkpoints on their GitHub.
| Repeating their results would be a good start.
| https://github.com/sapientinc/HRM
| cs702 wrote:
| The authors' code is at https://github.com/sapientinc/HRM .
|
| 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 wrote:
| 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.
| belter wrote:
| > If a professional reviewer spots a serious problem
|
| Did that ever happen? :-)
| atq2119 wrote:
| Of course. As usual, you tend to not hear about it when a
| system we rely on works well.
| yorwba wrote:
| 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 wrote:
| "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 wrote:
| 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 wrote:
| 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.
| sigmoid10 wrote:
| 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 wrote:
| 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 wrote:
| 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.
|
| https://github.com/sapientinc/HRM/blob/main/dataset/build_a
| r...
| bubblyworld wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| > _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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| If anything, they moved the goalpost closer to the
| starting line. I'm merely putting it back where it
| belongs.
| diwank wrote:
| 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.
| hodgehog11 wrote:
| 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 wrote:
| 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 wrote:
| Because publications is the number that academics have to
| make go up.
| hodgehog11 wrote:
| This and the exposure. There are so many papers on arXiv
| now that people often look to conference or journal
| publication lists.
| dleeftink wrote:
| The number has clearly ceased its function, so what are
| we chasing?
| gavinray wrote:
| Clout, funding, and employment I'd imagine?
| rapatel0 wrote:
| 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.
| frozenseven wrote:
| >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.
| riku_iki wrote:
| > 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.
| mitthrowaway2 wrote:
| 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.
| halayli wrote:
| The fact that you are expecting a paper just published to have
| been peer reviewed already tells me that you are likely not
| familiar with the process. The first step to have your work
| peer reviewed is to publish it.
| smokel wrote:
| 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.
|
| [1] https://github.com/sapientinc/HRM
| babel_ wrote:
| 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 wrote:
| 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 wrote:
| This is "The Bitter Lesson" of AI, no? "More compute beats
| clever algorithm."
| babel_ wrote:
| Quite the opposite, a clever algorithm needs less compute,
| and can leverage extra compute even more.
| gavmor wrote:
| Apologies, "clever" is a poor paraphrase of "domain-
| specific", or "methods that leveraged human
| understanding."[0]
|
| 0.
| http://www.incompleteideas.net/IncIdeas/BitterLesson.html
| smokel wrote:
| 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 wrote:
| Do you have a fork or the changes? I might take a look, and
| python dependency hell on Sunday is no good
| mkagenius wrote:
| > specify library versions explicitly
|
| Sometimes even that is not helpful. It's a pain we have to
| deal with.
| gavinray wrote:
| How is it not helpful?
|
| A dependency lock file with resolved versions for both
| direct and transient dependencies = reproducible build
| mkagenius wrote:
| 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.
| blincoln wrote:
| I don't know how common this is, but the fschat library
| maintainers went for at least a year without making an
| official release or updating the version number in their
| GitHub repo, so the only way to both have current code
| _and_ a reproducible build (without just including the
| fschat library directly, of course) was to pin it to a
| particular GitHub commit hash, which would get you code
| that was current, but with the version number from 12+
| months earlier.
|
| fschat is pretty popular for LLM-related work, so I
| assume this is at least not unheard-of for other notable
| third-party libraries.
| belter wrote:
| Is this not a variation of ReAct + Chain-of-Thought + Structured
| Planning? Or is that too unfair to the authors work?
|
| [1] - https://arxiv.org/abs/2210.03629
| SubiculumCode wrote:
| 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.
|
| [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4979567/
| malcontented wrote:
| 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 wrote:
| 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 wrote:
| > 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 wrote:
| The Universal Approximation Theorem.
| JBits wrote:
| 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.
| advael wrote:
| 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 wrote:
| 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.
| nowittyusername wrote:
| 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.
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