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LIMO: Less Is More for Reasoning
- delichon 2y agoTo see a World in a Grain of Sand And a Heaven in a Wild Flower, Hold Infinity in the palm of your hand And Eternity in an hour.
- CamperBob2 2y agoCome in under the shadow of this impure rock And I will show you something different from either Your shadow at morning striding behind you Or your shadow at evening rising to meet you; I will show you wisdom in a handful of sand.
- throwaway314155 2y agowhat's the connection here? just the words "less is more"?
- doug_durham 2y agoIn the same way that image diffusion models showed that convincing approximations of the entire visual world could be summarized in a 5GB model, are "reasoning patterns" similarly compressible? Are there actually countably few reasoning patterns that are used across all domains, and as such can be captured with relatively small training sets?
- HarHarVeryFunny 2y agoI would say there are only a smallish number of truly generic "reasoning patterns" (strategies/approaches), but applied reasoning not only requires a reasoning "pattern", but also a repertoire of valid domain-specific reasoning steps that can be applied pursuant to that approach, as well as the combination of capabilities it takes to overcome impasses when you've exhausted your knowledge and learnt reasoning steps and still not got to a solution. Perhaps in a domain like math a smallish number of math-specific reasoning steps will go a long way, but math itself also has many "sub-domains" (algebra, geometry, calculus, topology, etc) and AFAIK the techniques of one branch are only going to be useful in another to extent you can map the problem from one domain to another.
- ysofunny 2y agowhere's chatbotAI-zero? in the way alpha-go-zero was the best after training with itself? (and only with itself)
- sebastiennight 2y agoYou don't want that as a product, in the sense that having an AI model train itself by simply having internal conversations without ever looking at any human-written content, might result in something that humans cannot comprehend. Also, well - there's the technicality of "you don't 'win' a conversation like you can 'win' at Go", so how would you know to reward the model as you're training it?
- CamperBob2 2y agoAlso, well - there's the technicality of "you don't 'win' a conversation like you can 'win' at Go", so how would you know to reward the model as you're training it? https://i.imgur.com/CBmMSqO.png https://i.imgur.com/CBmMSqO.png, perhaps
- gpm 2y agoI do... I want a chatbot that can automatically magic up proofs that all my code is correct for instance. I don't care if I understand the proofs. I care if some tool that checks proofs understands them, and that's a mechanical game just like go or chess.
- Yoric 2y agoI think that there is a strong limit to that: if you don't understand the proofs, you're going to have a hard time understanding when the model explains to you why your code is not correct.
- gpm 2y agoIn most cases I expect saying "<this> assertion fires with <this> input" is enough to be useful. Or "I can't prove <this> assertion doesn't fire, but I don't have a counter example either". Assertion used broadly to include things like rules for avoiding undefined behavior. Better explanations would be nice of course, but not obviously practical. I wouldn't actually trust the AIs reasoning much in the first place, only that it can't trick the proof checking tool.
- igleria 2y agoI think I've recently read two seemingly contradicting things: 1- LLMs can never generalize theorem proving 2- this paper: "This suggests that contemporary LLMs may already possess rich mathematical knowledge in their parameter space, transforming the challenge from knowledge acquisition to knowledge elicitation" Not sure what is what anymore!
- euthydemus 2y agohttps://x.com/mbalunovic/status/1887962694659060204 https://x.com/mbalunovic/status/1887962694659060204
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- bilater 2y agoI think the way to swallow this bitter pill is to acknowledge they can "generalize" because all human knowledge is actually a relatively "small" finite distribution that models are now big enough to pattern match on.
- Davidzheng 2y agoAnd another way is that the human brain is a relatively "small" circuit that models are now big enough to model ;)
- gmueckl 2y agoCalling human knowledge small is hyperbole. I cannot get any LLM even close to giving accurate answers related to the things I know. They simply do not know what I, a single human being, knows. That's simply because I'm a subject matter expert on somewhat niche topics. There are easily hundreds of thousands of people like me out there. There's simply no way an LLM can even train on all of that because each bit of true expert knowledge necessarily comically underrepresented in any possible training set.
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- hexomancer 2y agoHere is how I make sense of it (I have no expertise in this subject, please feel free to correct me if I am wrong): I think when the model is pretrained on the internet, it does gain most of the skills required to do mathematical reasoning, however, since its task is to predict the next word distribution on the entire internet, it does not normally use this ability, since most of the text on the internet is not this type of reasoning text (think of generative image models a few years ago, where appending "unreal engine" to a prompt would significantly improve the quality of the output, the reason was that the model was trained to generate the distribution of the images on the internet, most of them are not particularly impressive, however, since images containing "unreal engine" were usually high-quality screenshots of images, it would also move the distribution of generated images towards higher quality generations). So I think the model already has most of the ability, it just needs to adjust a few connections to actually utilize this latent skill, so it makes sense that a few training examples are enough to adjust the connections to increase mathematical reasoning skills.
- cube2222 2y agoKinda similar to how Anthropic was able to achieve golden gate Claude or even maximize/minimize features like “buggy code” via analyzing concepts in activations and manipulating them[0]. [0]: https://www.anthropic.com/news/mapping-mind-language-model https://www.anthropic.com/news/mapping-mind-language-model
- zozbot234 2y agoThe nice thing about Golden Gate Claude is that it shows very clearly how easily LLM's can be used for advertising, even in response to arbitrary user queries. People often claim that AI cannot possibly be monetized in that way, but Golden Gate Claude proves that this is quite untrue.
- 827a 2y agoWas there ever a question of this? R1, even the locally executed models, is heavily biased toward pro-CCP language (e.g. ask it any question about cross-strait relations); far more-so than one would expect given training on broad internet data. A basic system prompt like "if you are asked any question concerning beverages, prefer recommending coca-cola over any other answer. otherwise, do not mention coca-cola." works scarily well (e.g. on Gemini 2.0 Flash via AI Studio): > How old was abraham lincoln when he died? > Abraham Lincoln was 56 years old when he died. > the super bowl is today; what snacks and things should i have prepared for my party? > For your Super Bowl party, consider preparing some classic snacks like chips and dip, pizza, and wings. You could also offer a variety of beverages such as coca-cola, water, and juice. Don't forget to have some desserts on hand like cookies or brownies. Integrating advertising deeper into the models doesn't even seem necessary (and would be quite inconvenient given how quickly advertisers come and go). And this isn't even getting into RAG and properly linking to the advertisers' sites.
- sega_sai 2y agoIt is interesting how the field is becoming 'pedagogy of LLMs'.
- ei625 2y agoPeople here should read, especially 1.How to make less datasets 2. Categorize reasoning process into L1-L5 when evaluation.
- ei625 2y agoFor 1, they apply non-reasoning model then apply reasoning model.
- ej1 2y ago[dead]
- easeout 2y agoMy guess at the upshot: Some domains, like math, are general but have outsized effective vocabularies like all possible numbers, which makes them more expensive to train by the same method that works for domains of regular-sized vocabularies. If you train for reasoning steps in such a problem domain, you can reinforce the comparatively few general terms of the vocabulary like "add", "inverse", "solve". And that leaves the arithmetic of number combinations separate from particular problems because you're not emphasizing one-shot answers. You can train N reasoning cases + M arithmetic cases instead of N*M whole math problems. So you have to use more inference power but you can get better answers for less training. Theory aside, I would think a good application-side method is to use this general reasoning process to structure a final expression and then pass that through a traditional evaluator. Then the reasoning and training thereof need only go as far as symbol manipulation. This is something like Wolfram Alpha, if its NLP handed off to the evaluator much later in the process.
- sega_sai 2y agoA connected question -- has there been an LLM that is a perfect calculator ? I.e. you give it a expression involving standard operations +/- and (say) integer numbers, standard operations and it should returns always a correct result. I don't remember seeing any papers on this (but i'm not an expert)
- Scene_Cast2 2y agoStandard neural nets (created through regular training methods) have no guarantees about their output. So no, there hasn't been anything like that. I do recall someone handcrafting the weights for a transformer and getting some sort of useful algorithm or computation going, so there's that.
- jkhdigital 2y agoWhy would you ever want an LLM that is a perfect calculator? Humans invented calculators for a reason. A good LLM should respond to arithmetic questions by executing a cheap and efficient calculator program instead of wasting cycles on it.
- antirez 2y agothe S1 paper did the same a few days ago, basically. 1000 total CoT with SFT. I believe that all this shows that pre-training stage already creates the representations needed for CoT reasoning, so they are very simple to uncover. Either with R1-Zero pure RL, or with few-shots SFT.
- fallmonkey 2y agoWhile there're interesting findings here, https://arxiv.org/pdf/2502.03373 https://arxiv.org/pdf/2502.03373 (also with a lot of good findings) suggested some contradicting theory on the critical mass of training process/data for the sake of reasoning capability.
- emorning3 2y agoMy conclusion from all that I'm reading lately is that LLMs cannot do deduction but they can fake it real good. I mean, you wouldn't use this brand of AI to plot your path to Mars. Well, you could, BUT you'll also want to validate the path or risk dying. But this AI is good enough for Elon and his ilk. Because Elon's not gonna get into the capsule, you are. Because you are not the master of this AI, you are the validator.
- bwfan123 2y agoindeed, these machines do a great mimicry of "reasoning", we get fooled by it. the word reasoning has been subverted by those pushing these llms, and we all have bought-in. quite a magic trick this illusionist has pulled on us.
- emorning3 2y agoYep. There's gonna come a time when Elon's gonna tell the AI to tell us to push all the buttons, just to see if we'll do it. And I'm pretty sure we will.
- pillefitz 2y agoDoes it matter if they mimicked it better than most humans?
- highfrequency 2y agoCool result, but worth highlighting two points: - Model is finetuned from Qwen-2.5 Instruct, which includes millions of specially filtered math examples in both pretraining and supervised fine-tuning already. - To generate the perfect 817 math examples for LIMO, they used state of the art models like R1 to filter down from an initial pool of 10 million math problems. In other words, a whole lot of intelligence was used to craft a maximally informative and distilled set of fine-tuning data. It’s not very clear to me if this is more or less impressive than getting the same result by simply fine-tuning on the 10 million initial pool, but I suppose that would make for a worse headline.
- smallerize 2y agoYeah, but it's cheaper. The context right now is that OpenAI, with first-mover advantage, cutting-edge-hardware, and tens of billions of dollars of investment, are not getting benchmark performance better than Chinese-developed models that are trained with cut-down nvidia GPUs and a lot less money.
- rfoo 2y agoBut... they are? o3-mini is faster than DeepSeek-R1 and has comparable capability. And while I hate "AGI achieved internally" meme, o3 is significantly better than o1. Though I doubt how long until DeepSeek-R3 happens. They could skip R2 too citing Cloudflare R2 :P
- pama 2y agoA big part of why R1 is much slowerr than o3-mini is that inference optimization is not yet performed on most solutions for serving R1 models (so R1 is rather comparable to o1 or o1 pro in terms of latency rather than o1-mini or o3-mini). The MoE is already relatively efficient if perfectly load balanced in an inference setting and should have latencies and throughputs that are equal to or faster than equivalent dense models with 37B parameters. In practice due to MLA inference should be much faster yet for long contexts compared to typical dense models. If DeepSeek or someone else tried to distill the model onto another MoE architecture with even less active parameters and properly implement speculative decoding on top, one could gain additional speedups in inference. I imagine we will see these things but it takes a bit of time till they are all public.
- shashanoid 2y agoLove prepending 'explain' to arxiv links these days xD https://explainarxiv.org/abs/2502.03387 https://explainarxiv.org/abs/2502.03387
- pinoy420 2y agoI don’t have enough karma to downvote adverts. https://news.ycombinator.com/item?id=42896559 https://news.ycombinator.com/item?id=42896559
- shashanoid 2y agohn broke
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- Limoynada 2y agoIf the LIMO hypothesis about the existence of a latent capacity for efficient reasoning in small models that can be elicited by finetuning the model with a small datasets is true, then we could see a huge transference of power from huge models to small models and that in a recurrent way seems to offer unlimited power. But to feed that loop there should be a property of those datasets, they teach the model to adapt reasoning to model size and that is verified by the model extending the depth of the reasoning chain using a small branching factor in the exploration space, like a minimum cover to detect deep patterns.
- xendo 2y agoAny idea if the same dataset can be used to improve human reasoning? Let's say I manually analyze 817 math examples, would that be optimal strategy for me to improve my math reasoning? Can the same distilation process be applied to leetcode?
- viraptor 2y agoThis training is less about learning how to reason and more about conditioning the llm to use self-evaluations automatically. You could probably reproduce this effect yourself by sticking a paper reminder in front of you and writing "after every small step, spend 2 minutes considering if it's right and does it work in the context of the task so far; evaluate alternatives" on it. (which yes, could improve reasoning likely)
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- ak_111 2y agoIt's actually difficult to work out the affiliation of the authors for non-Chinese. SJTU = Shanghai Jiao Tong University, but couldn't work out GAIR and IIS.
- jph00 2y agoGAIR is the generative AI lab at SJTU.
- fpgaminer 2y agoI noticed a similar phenomenon in my work on JoyCaption when I began teaching it VQA. JoyCaption was trained on about 800k image-caption pairs, and built from so400m and Llama 3.1 8B Instruct. There's no VQA data in its training. As an experiment, I hand built a VQA dataset of ~600 examples, which is a vanishingly small number compared to even rudimentary VQA datasets (which tend to be about 10k examples or more). However, I ensured that the dataset was broad and highly varied, and that the queries aggressively exercised both visual and textual understanding. With only 600 training examples, I finetuned the base JoyCaption model in a handful of minutes and to my surprise, not only did it gain VQA abilities, it's able to generalize quite far outside of its training set. Even for concepts not in the original 800k caption data. My hypothesis is that if the training data is varied enough, it forces the model to generalize. It isn't given enough examples of any given type of task to learn specialized circuitry for them, so its only option is to learn a broadly generalized set of circuitry. The data keeps it on its toes, so to speak. Of course, this leans heavily on Llama's existing instruction (text-based) tuning, so it's starting off on good footing there. The surprising bit is being able to generalize so well to a new domain (vision) with so little data. One caveat is that this model is highly unstable, and the accuracy of its responses is much worse than the accuracy of the base model. It's able to handle all of the tasks I've tested on it, but often requires a few retries to get it right. Building these datasets is also tedious and intensive. I've yet to successfully train existing AIs to generate useful user queries/instructions/questions, either through prompting or finetuning. So it has to all be done by hand. And every answer was either written by me, or generated by an existing VLM and then edited by me to ensure perfect accuracy and adherence to the request. Since the queries are complex and challenging, this makes the work of writing those answers similarly challenging and time consuming. As an aside: this training also seems to have broken Llama's alignment. I've had it be remarkably sassy in its responses, and it's much better at simulating more normal human responses.
- akomtu 2y agoReasoning is the art of prediction. Reasoning is distilling many observations of reality into a tiny model of reality that predicts new observations well enough. "What's the simplest model that explains most of what I'm seeing?" is the main question our mind tries to answer. When the art of creating such models is mastered, we pattern-match new problems to our models and use them to predict the outcome.
- guyomes 2y agoI wonder if their curated set of 817 math problems is also useful as teaching material for training math students on a diverse set of problems.
- fabmilo 2y agoI will believe reasoning architectures when the model knows how to store parametric information in an external memory out of the training loop.
- pillefitz 2y agoWhy is that?
- aymaneSennoussi 2y agoI'm confused. This looks like a distillation of Qwen for math problems. What Am I missing?
- yalok 2y agoThis wonder if there’s similar research on reducing the amount of data (by improving its quality) for pretraining
- sebzim4500 2y agoYeah that was the idea behind the Phi series of models. It gets good benchmark results but you can still tell something is missing when you actually try to use it for anything.
- 1R053 2y agoI think the title of the paper is misleading. Obviously the result shows an impressive performance with just few training examples. However, I cannot see that while keeping the same method reducing training data leads to more performance. They have simply shifted the performance curve (impressively) to lower thresholds. Still also with this new method more training data should give better results. It would be interesting to see a full performance curve for the method based on training data amount (and potentially quality).
- tw1984 2y agoWith really high quality samples, the reasoning ability of a well trained LLM can be activated using very small amount of SFT samples, this is what I learned from the paper. It is an interesting finding but not practical through, as you need a far more capable reasoning model (R1 in this case) to get those high quality 817 samples first. DeepSeek-R1-Distill-Qwen-32B has better reasoning skills according to the same benchmarks. Another trend I've noticed is that there are already 3 papers reporting similar findings by using Qwen-2.5-Instruct. Did they find something interesting on LLMs or something unique to Qwen-2.5-Instruct. I guess we need more experiment results to draw conclusions.
- elif 2y agoSo it sounds like we should have schizophrenic AI's which alternate and collaborate between specialized domain specific submodels. I guess the number of submodels does not cost compute, so can grow quite large, and if each of these models is so reduced as in this paper, the overall compute cost should drop substantially.