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Hierarchical Reasoning Model
- torginus 1y agoIs it just me or are symbolic (or as I like to call it 'video game') AI is seeping back into AI?
- taylorius 1y agoPerhaps so - but represented in a trainable, neural form. Very exciting!
- cornholio 1y agoNatural general intelligences sure seem to work this way.
- marcosdumay 1y agoTraining databases is much easier than training neural networks.
- bobosha 1y agoBut symbolic != hierarchical
- cs702 1y agoBased 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 1y agoExactly! > 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 1y agoIs it talking about fine tuning existing models with 1000 examples to beat them in those tasks?
- cs702 1y agoYeah, that was pretty much my reaction. I will need time on a computer too. The repo is at https://github.com/sapientinc/HRM 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 1y agoSame! Guan’s work on sample packing during finetuning has become a staple. His openchat code is also super simple and easy to understand.
- lumost 1y agoI 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 1y agobut does it scale?
- lispitillo 1y agoI 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 1y agomust say I am suspicious in this regard, as they don't show applications other than a Sudoku solver and don't discuss downsides.
- Oras 1y agoand 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 1y agoNot only on Sudoku, there is also maze solving and ARC-AGI.
- JBits 1y agoI 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.
- OgsyedIE 1y agoSkimming 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 1y agoGoodbye captchas I guess? Somehow they are still around.
- topspin 1y ago> "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 1y agoAs soon I read the hlm/llm split, it immediately reminded me of the human brain.
- JonathanRaines 1y agoI 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/leaderboard https://www.kaggle.com/competitions/arc-prize-2025/leaderboa...)
- d4rkn0d3z 1y agoSkepticism 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 1y ago24 hours on a 4070. Seems quite doable. https://github.com/sapientinc/HRM https://github.com/sapientinc/HRM
- suspended_state 1y ago> Experiments below assume an 8-GPU setup. Careful with those numbers.
- JonathanRaines 1y agoNice! They provide trained checkpoints on their GitHub. Repeating their results would be a good start. https://github.com/sapientinc/HRM https://github.com/sapientinc/HRM
- cs702 1y agoThe authors' code is at https://github.com/sapientinc/HRM 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!
- deleted 1y ago[deleted]
- smokel 1y agoIf 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 https://github.com/sapientinc/HRM
- babel_ 1y agoAlphaZero 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 1y agoThanks 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 1y agoThis is "The Bitter Lesson" of AI, no? "More compute beats clever algorithm."
- babel_ 1y agoQuite the opposite, a clever algorithm needs less compute, and can leverage extra compute even more.
- gavmor 1y agoApologies, "clever" is a poor paraphrase of "domain-specific", or "methods that leveraged human understanding."[0] 0. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- belter 1y agoIs 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 https://arxiv.org/abs/2210.03629
- mitch_said 1y ago[dead]
- SubiculumCode 1y agoAs 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/ https://pmc.ncbi.nlm.nih.gov/articles/PMC4979567/
- malcontented 1y agoI 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 1y agoYes, 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 1y 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 1y agoThe Universal Approximation Theorem.
- advael 1y agoI 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 1y agoThis 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 1y agoI'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.
- aramend 1y ago> For ARC-AGI challenge, we start with all input-output example pairs in the training and the evalua- tion sets ... At test time, we proceed as follows for each test input in the evaluation set: ... Very often I see people misuse the ARC-AGI data when training. The input examples in the evaluation set are not intended for training your AI system. It is a downside of ARC that its data is (somehow?) complicated enough for the clever people building AI systems to miss the point, and people report and compare results as a single percentage where the data mix used for training may not make the comparison applicable.
- christildizzl 1y agoSomebody successful in reproducing the results?
- kevinwang 1y agoAm I the only one that doesn't get this at all? Why use a recurrent architecture at all if you're only going to backprop through 1 step?