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The universal weight subspace hypothesis
- zkmon 9mo agoSomething tells me this is probably as important as the "attention is all you need".
- deleted 9mo ago[deleted]
- CGMthrowaway 9mo agoThey compressed the compression? Or identified an embedding that can "bootstrap" training with a headstart ? Not a technical person just trying to put it in other words.
- vlovich123 9mo agoThey identified that the compressed representation has structure to it that could potentially be discovered more quickly. It’s unclear if it would also make it easier to compress further but that’s possible.
- mapontosevenths 9mo agoTo use an analogy: Imagine a spreadsheet with 500 smoothie recipes one in each row, each with a dozen ingredients as the columns. Now imagine you discover that all 500 are really just the same 11 base ingredients plus something extra. What they've done here is use SVD, (which is normally used for image compression and noise reduction), to find that "base recipe". Now we can reproduce those other recipes by only recording the one igredient that differs. More interestingly it might tell us something new about smoothies in general to know that they all share a common base. Maybe we can even build a simpler base using this info. At least in theory. The code hasn't actually been released yet. https://toshi2k2.github.io/unisub/#key-insights https://toshi2k2.github.io/unisub/#key-insights
- CGMthrowaway 9mo agoYeah that's pretty much how I understood it. Good analogy. We are finding the French Mother Sauce. Reading the comments it seems everyone is still clear on the practical implications of that.
- canjobear 9mo agoWhat's the relationship with the Platonic Representation Hypothesis?
- MarkusQ 9mo agoFrom what I can tell, they are very closely related (i.e. the shared representational structures would likely make good candidates for Platonic representations, or rather, representations of Platonic categories). In any case, it seems like there should be some sort of interesting mapping between the two.
- brillcleaner 9mo agoMy first thought was that this was somehow distilling universal knowledge. Platonic ideals. Truth. Beauty. Then I realized- this was basically just saying that given some “common sense”, the learning essence of a model is the most important piece, and a lot of learned data is garbage and doesn’t help with many tasks. That’s not some ultimate truth, that’s just optimization. It’s still a faulty LLM, just more efficient for some tasks.
- unionjack22 9mo agoI hope someone much smarter than I answers this. I’ve been noticing an uptick platonic and neo-platonic discourse in the zeitgeist and am wondering if we’re converging on something profound.
- nowittyusername 9mo agoI've been noticing that as well....
- deleted 9mo ago[deleted]
- altairprime 9mo agoSame hat, except 18 months later, assuming it survives peer review, reproduction, etc. (or: "The newer one proposes evidence that appears to support the older one.") https://arxiv.org/abs/2405.07987 https://arxiv.org/abs/2405.07987
- kacesensitive 9mo agointeresting.. this could make training much faster if there’s a universal low dimensional space that models naturally converge into, since you could initialize or constrain training inside that space instead of spending massive compute rediscovering it from scratch every time
- odyssey7 9mo agoOr an architecture chosen for that subspace or some of its properties as inductive biases.
- bigbuppo 9mo agoWouldn't this also mean that there's an inherent limit to that sort of model?
- rhaen 9mo agoNot strictly speaking? A universal subspace can be identified without necessarily being finite. As a really stupid example: the sets of integers less than 2, 8, 5, and 30 can all be embedded in the set of integers less than 50, but that doesn’t require that the set of integer is finite. You can always get a bigger one that embeds the smaller.
- scotty79 9mo ago> Wouldn't this also mean that there's an inherent limit to that sort of model? If all need just 16 dimensions if we ever make one that needs 17 we know we are making progress instead of running in circles.
- VikingCoder 9mo agoI find myself wanting genetic algorithms to be applied to try to develop and improve these structures... But I always want Genetic Algorithms to show up in any discussion about neural networks...
- EvanAnderson 9mo agoI have a real soft spot for the genetic algorithm as a result of reading Levy's "Artificial Life" when I was a kid. The analogy to biological life is more approachable to my poor math education than neural networks. I can grok crossover and mutation pretty easily. Backpropagation is too much for my little brain to handle.
- DennisP 9mo agoI do too, and for the same reasons. Levy's book had a huge impact on me in general.
- nrhrjrjrjtntbt 9mo agoBackprop is learnable through karpathy videos but it takes a lot of patience. The key thing is the chain rule. Get that and the rest is mostly understanding what the bulk operations on tensors are doing (they are usually doing something simple enough but so easy to make mistakes)
- embedding-shape 9mo ago> Backpropagation is too much for my little brain to handle. I just stumbled upon a very nice description of the core of it, right here: https://www.youtube.com/watch?v=AyzOUbkUf3M&t=133s https://www.youtube.com/watch?v=AyzOUbkUf3M&t=133s Almost all talks by Geoffrey Hinton (left side on https://www.cs.toronto.edu/~hinton/ https://www.cs.toronto.edu/~hinton/) are in very approachable if you're passingly familiar with some ML.
- acjohnson55 9mo agoYou can definitely understand backpropagation, you just gotta find the right explainer. On a basic level, it's kind of like if you had a calculation for aiming a cannon, and someone was giving you targets to shoot at 1 by 1, and each time you miss the target, they tell you how much you missed by and what direction. You could tweak your calculation each time, and it should get more accurate if you do it right. Backpropagation is based on a mathematical solution for how exactly you make those tweaks, taking advantage of some calculus. If you're comfortable with calculus you can probs understand it. If not, you might have some background knowledge to pick up first.
- api 9mo agoI immediately started thinking that if there are such patterns maybe they capture something about the deeper structure of the universe.
- EvanAnderson 9mo agoOn a hike this weekend my daughter and I talked about the similarities of the branching and bifurcating patterns in the melting ice on a pond, the branches of trees, still photos of lightning, the circulatory system, and the filaments in fractals.
- deleted 9mo ago[deleted]
- api 9mo agoFind some images of the entire huge scale structure of the universe. It looks a bit like… a brain. What does this mean? Probably not nothing, but probably not “the cosmos is the mind of god.” It probably means that we live in a universe that tends to produce repeating nested patterns at different scales. But maybe that’s part of what makes it possible to evolve or engineer brains that can understand it. If it had no regularity there’d be no common structural motifs.
- EvanAnderson 9mo agoSimilar feeling here re: "mind of God". I interpret these patterns as a very simple property of mathematics producing complex-looking patterns and evolution exploiting that complexity. Evolution is the ultimate procedural content generation machine.
- kroaton 9mo agoI thought that was Houdini.
- mwkaufma 9mo ago(Finds a compression artifact) "Is this the meaning of consciousness???"
- farhanhubble 9mo agoWould you see a lower rank subspace if the learned weights were just random vectors?
- imtringued 9mo agoThis is a good point, but I think this only works for D*A, where D=Sigma is a diagonal matrix with learnable parameters. It probably doesn't work for a full singular value decomposition (SVD) UDV^T. Basically, what if we're not actually "training" the model, but rather the model was randomly initialized and the learning algorithm is just selecting the vectors that happen to point into the right direction? A left multiplication of the form D*A with a diagonal matrix is equivalent to multiplying each row in A with the corresponding diagonal element. Low values mean the vector in question was a lottery blank and unnecessary. High values means that this turns out to be correct vector, yay! But this trivial explanation doesn't work for the full SVD, because you now have a right multiplication U*D. This means each column gets multiplied against the corresponding diagonal element. Both the column in U and row vector in V^T have to perfectly coincide to make the "selection" theory work, which is unlikely to be true for small models, which happen to work just fine.*
- ibgeek 9mo agoThey are analyzing models trained on classification tasks. At the end of the day, classification is about (a) engineering features that separate the classes and (b) finding a way to represent the boundary. It's not surprising to me that they would find these models can be described using a small number of dimensions and that they would observe similar structure across classification problems. The number of dimensions needed is basically a function of the number of classes. Embeddings in 1 dimension can linearly separate 2 classes, 2 dimensions can linearly separate 4 classes, 3 dimensions can linearly separate 8 classes, etc.
- mlpro 9mo agoThe analysis is on image classification, LLMs, Diffusion models, etc.
- AIorNot 9mo agoInteresting - I wonder if this ties into the Platonic Space Hypothesis recently being championed by computational biologist Mike Levin E.g https://youtu.be/Qp0rCU49lMs?si=UXbSBD3Xxpy9e3uY https://youtu.be/Qp0rCU49lMs?si=UXbSBD3Xxpy9e3uY https://thoughtforms.life/symposium-on-the-platonic-space/ https://thoughtforms.life/symposium-on-the-platonic-space/ e.g see this paper on Universal Embeddings https://arxiv.org/html/2505.12540v2 https://arxiv.org/html/2505.12540v2 "The Platonic Representation Hypothesis [17] conjectures that all image models of sufficient size have the same latent representation. We propose a stronger, constructive version of this hypothesis for text models: the universal latent structure of text representations can be learned and, furthermore, harnessed to translate representations from one space to another without any paired data or encoders. In this work, we show that the Strong Platonic Representation Hypothesis holds in practice. Given unpaired examples of embeddings from two models with different architectures and training data, our method learns a latent representation in which the embeddings are almost identical" Also from the OP's Paper we see this on statement: "Why do these universal subspaces emerge? While the precise mechanisms driving this phenomenon remain an open area of investigation, several theoretical factors likely contribute to the emergence of these shared structures. First, neural networks are known to exhibit a spectral bias toward low frequency functions, creating a polynomial decay in eigenvalues that concentrates learning dynamics into a small number of dominant directions (Belfer et al., 2024; Bietti et al., 2019). Second, modern architectures impose strong inductive biases that constrain the solution space: convolutional structures inherently favor local, Gabor-like patterns (Krizhevsky et al., 2012; Guth et al., 2024), while attention mechanisms prioritize recurring relational circuits (Olah et al., 2020; Chughtai et al., 2023). Third, the ubiquity of gradient-based optimization – governed by kernels that are largely invariant to task specifics in the infinite-width limit (Jacot et al., 2018) – inherently prefers smooth solutions, channeling diverse learning trajectories toward shared geometric manifolds (Garipov et al., 2018). If these hypotheses hold, the universal subspace likely captures fundamental computational patterns that transcend specific tasks, potentially explaining the efficacy of transfer learning and why diverse problems often benefit from similar architectural modifications."
- unionjack22 9mo agoDr. Levin’s work is so fascinating. Glad to see his work referenced. If anyone wishes to learn more while idle or commuting, check out Lex Friedman’s podcast episode with him linked above
- altairprime 9mo agoFor those trying to understand the most important parts of the paper, here's what I think is the most significant two statements, subquoted out of two (consecutive) paragraphs midway through the paper: > we selected five additional, previously unseen pretrained ViT models for which we had access to evaluation data. These models, considered out-of-domain relative to the initial set, had all their weights reconstructed by projecting onto the identified 16-dimensional universal subspace. We then assessed their classification accuracy and found no significant drop in performance > we can replace these 500 ViT models with a single Universal Subspace model. Ignoring the task-variable first and last layer [...] we observe a requirement of 100 × less memory, and these savings are prone to increase as the number of trained models increases. We note that we are, to the best of our knowledge, the first work, to be able to merge 500 (and theoretically more) Vision Transformer into a single universal subspace model. This result implies that hundreds of ViTs can be represented using a single subspace model So, they found an underlying commonality among the post-training structures in 50 LLaMA3-8B models, 177 GPT-2 models, and 8 Flan-T5 models; and, they demonstrated that the commonality could in every case be substituted for those in the original models with no loss of function; and noted that they seem to be the first to discover this. For a tech analogy, imagine if you found a bzip2 dictionary that reduced the size of every file compressed by 99%, because that dictionary turns out to be uniformly helpful for all files. You would immediately open a pull request to bzip2 to have the dictionary built-in, because it would save everyone billions of CPU hours. [*] [*] Except instead of 'bzip2 dictionary' (strings of bytes), they use the term 'weight subspace' (analogy not included here[**]) — and, 'file compression' hours becomes 'model training' hours. It's just an analogy. [**] 'Hilbert subspaces' is just incorrect enough to be worth appending as a footnote[***]. [***] As a second footnote.
- westoncb 9mo ago> So, they found an underlying commonality among the post-training structures in 50 LLaMA3-8B models, 177 GPT-2 models, and 8 Flan-T5 models; and, they demonstrated that the commonality could in every case be substituted for those in the original models with no loss of function; and noted that they seem to be the first to discover this. Could someone clarify what this means in practice? If there is a 'commonality' why would substituting it do anything? Like if there's some subset of weights X found in all these models, how would substituting X with X be useful? I see how this could be useful in principle (and obviously it's very interesting), but not clear on how it works in practice. Could you e.g. train new models with that weight subset initialized to this universal set? And how 'universal' is it? Just for like like models of certain sizes and architectures, or in some way more durable than that?
- pagekicker 9mo ago[flagged]
- 100721 9mo agoActing as a pass-through for LLMs is logically equivalent to wiring up a bot account.
- pagekicker 9mo agoNo, it's not, unless you can argue that the bot would have thought of asking the same question I did, which is unlikely.
- 100721 9mo agoLet’s define the bot as one that asks LLMs to visualize concepts, then. Now I’ve argued that the bot would very likely have thought of the same question you did, and my original assertion stands.
- IAmBroom 9mo ago"I asked [AI] and it said..." is not the path to social acceptance in this herd.
- masteranza 9mo agoIt's basically way better than LoRA under all respects and could even be used to speed up inference. I wonder whether the big models are not using it already... If not we'll see a blow up in capabilities very, very soon. What they've shown is that you can find the subset of parameters responsible for transfer of capability to new tasks. Does it apply to completely novel tasks? No, that would be magic. Tasks that need new features or representations break the method, but if it fits in the same domain then the answer is "YES". Here's a very cool analogy from GPT 5.1 which hits the nail in the head in explaining the role of subspace in learning new tasks by analogy with 3d graphics. Think of 3D character animation rigs: • The mesh has millions of vertices (11M weights). • Expressions are controlled via: • “smile” • “frown” • “blink” Each expression is just: mesh += α_i \* basis_expression_i Hundreds of coefficients modify millions of coordinates.
- mlpro 9mo agoIt does seem to be working for novel tasks.
- topspin 9mo ago> Does it apply to completely novel tasks? No, that would be magic. Are there novel tasks? Inside the limits of physics, tasks are finite, and most of them are pointless. One can certainly entertain tasks that transcend physics, but that isn't necessary if one merely wants an immortal and indomitable electronic god.
- janalsncm 9mo agoWithin the context of this paper, novel just means anything that’s not a vision transformer.
- odyssey7 9mo agoNow that we know about this, that the calculations in the trained models follow some particular forms, is there an approximation algorithm to run the models without GPUs?
- nothrowaways 9mo agoWhat if all models are secretly just fine tunes of llama?
- nextworddev 9mo agoThe central claim, or "Universal Weight Subspace Hypothesis," is that deep neural networks, even when trained on completely different tasks (like image recognition vs. text generation) and starting from different random conditions, tend to converge to a remarkably similar, low-dimensional "subspace" in their massive set of weights.
- deleted 9mo ago[deleted]
- nothrowaways 9mo ago> Principal component analysis of 200 GPT2, 500 Vision Transformers, 50 LLaMA- 8B, and 8 Flan-T5 models reveals consistent sharp spectral decay - strong evidence that a small number of weight directions capture dominant variance despite vast differences in training data, objectives, and initialization. Isn't it obvious?
- stingraycharles 9mo agoWell intuitively it makes sense that within each independent model, a small number of weights / parameters are very dominant, but it’s still super interesting that these can be swapped between all the models without loss of performance. It isn’t obvious that these parameters are universal across all models.
- mlpro 9mo agoNot really. If the models are trained on different dataset - like one ViT trained on satellite images and another on medical X-rays - one would expect their parameters, which were randomly initialized to be completely different or even orthogonal.
- crooked-v 9mo agoNow I wonder how much this "Universal Subspace" corresponds to the same set of scraped Reddit posts and pirated books that apparently all the bigcorps used for model training. Is it 'universal' because it's universal, or because the same book-pirating torrents got reused all over?
- energy123 9mo agoEvery vision task needs edge/contrast/color detectors and these should be mostly the same across ViTs, needing only a rotation and scaling in the subspace. Likewise with language tasks and encoding the basic rules of language which are the same regardless of application. So it is no surprise to see intra-modality shared variation. The surprising thing is inter-modality shared variation. I wouldn't have bet against it but I also wouldn't have guessed it. I would like to see model interpretability work into whether these subspace vectors can be interpreted as low level or high level abstractions. Are they picking up low level "edge detectors" that are somehow invariant to modality (if so, why?) or are they picking up higher level concepts like distance vs. closeness?
- horsepatties 9mo agoI hope that this leads to more efficient models. And it’s intuitive- it seems as though you could find the essence of a good model and a model reduced to that essence would be more efficient. But, this is theoretical. I can also theorize flying cars- many have, it seems doable and achievable, but yet I see no flying cars on my way to work.
- deleted 9mo ago[deleted]
- hn_throwaway_99 9mo agoI read the abstract (not the whole paper) and the great summarizing comments here. Beyond the practical implications of this (i.e. reduced training and inference costs), I'm curious if this has any consequences for "philosophy of the mind"-type of stuff. That is, does this sentence from the abstract, "we identify universal subspaces capturing majority variance in just a few principal directions", imply that all of these various models, across vastly different domains, share a large set of common "plumbing", if you will? Am I understanding that correctly? It just sounds like it could have huge relevance to how various "thinking" (and I know, I know, those scare quotes are doing a lot of work) systems compose their knowledge.
- gedy 9mo agoIt could, though maybe "just" in a similar way that human brains are the same basic structure.
- themaxice 9mo agoSomewhat of a tangent, but if you enjoy the philosophy of AI and mathematics, I highly recommend reading Gödel, Escher, Bach: an Eternal Golden Braid by D. Hofstadter. It is primarily about the Incompleteness Theorem, but does touch on AI and what we understand as being an intelligence
- inciampati 9mo agoThe authors study a bunch of wild low rank fine tunes and discover that they share a common... low rank! ... substructure which is itself base model dependent. Humans are (genetically) the same. You need only a handful of PCs to represent the cast majority of variation. But that's because of our shared ancestry. And maybe the same thing is going on here.
- modeless 9mo agoThis seems confusingly phrased. When they say things like "500 Vision Transformers", what they mean is 500 finetunes of the same base model, downloaded from the huggingface accounts of anonymous randos. These spaces are only "universal" to a single pretrained base model AFAICT. Is it really that surprising that finetunes would be extremely similar to each other? Especially LoRAs? I visited one of the models they reference and huggingface says it has malware in it: https://huggingface.co/lucascruz/CheXpert-ViT-U-MultiClass https://huggingface.co/lucascruz/CheXpert-ViT-U-MultiClass
- mlpro 9mo agoWhy would they be similar if they are trained on very different data? Also, trained from scratch models are also analyzed, imo.
- deleted 9mo ago[deleted]
- modeless 9mo agoThey are trained on exactly the same data in the same order with the same optimizer because they are literally the same base model. With a little fine tuning added on top. I see now that they did one experiment with trained from scratch models. They trained five Resnet-50s on five disjoint datasets of natural images, most quite small. And IIUC they were able to, without further training, combine them into one "universal" model that can be adapted to have only somewhat worse performance on any one of the five datasets (actually one of them is pretty bad) using only ~35 adaptation parameters. Which is kind of cool I guess but I also don't find it that surprising? I don't expect that you'd get the same finding at large scale in LLMs trained from scratch on disjoint and dissimilar data with different optimizers etc. I would find that surprising. But it would be very expensive to do that experiment so I understand why they weren't able to.
- mlpro 9mo agoThey are not trained on the same data. Even a skim of the paper shows very disjoint data. The LLMs are finetuned on very disjoint data. I checked some are on Chinese and other are for Math. The pretrained model provides a good initialization. I'm convinced.
- alyxya 9mo agoI’ve had a hard time parsing what exactly the paper is trying to explain. So far I’ve understood that their comparison seems to be models within the same family and same weight tensor dimensions, so they aren’t showing a common subspace when there isn’t a 1:1 match between weight tensors in a ViT and GPT2. The plots showing the distribution of principal component values presumably does this on every weight tensor, but this seems to be an expected result that the principal component values shows a decaying curve like a log curve where only a few principal components are the most meaningful. What I don’t get is what is meant by a universal shared subspace, because there is some invariance regarding the specific values in weights and the directions of vectors in the model. For instance, if you were doing matrix multiplication with a weight tensor, you could swap two rows/columns (depending on the order of multiplication) and all that would do is swap two values in the resulting product, and whatever uses that output could undo the effects of the swap so the whole model has identical behavior, yet you’ve changed the direction of the principal components. There can’t be fully independently trained models that share the exact subspace directions for analogous weight tensors because of that.
- seeknotfind 9mo agoYeah, it sounds platonic the way it's written, but it seems more like a hyped model compression technique.
- RandyOrion 9mo ago> From their project page: > We analyze over 1,100 deep neural networks—including 500 Mistral-7B LoRAs and 500 Vision Transformers. We provide the first large-scale empirical evidence that networks systematically converge to shared, low-dimensional spectral subspaces, regardless of initialization, task, or domain. I instantly thought of muon optimizer which provides high-rank gradient updates and Kimi-k2 which is trained using muon, and see no related references. The 'universal' in the title is not that universal.
- lucid-dev 9mo agoPretty funny if you ask me. Maybe we can start to realize now: "The common universal subspace between human individuals makes it easier for all of them to do 'novel' tasks so long as their ego and personality doesn't inhibit that basic capacity." And that: "Defining 'novel' as 'not something that you've said before even though your using all the same words, concepts, linguistic tools, etc., doesn't actually make it 'novel'" Point being, yeah duh, what's the difference between what any of these models are doing anyway? It would be far more surprising if they discovered a *different* or highly-unique subspace for each one! Someone gives you a magic lamp and the genie comes out and says "what do you wish for"? That's still the question. The question was never "why do all the genies seem to be able to give you whatever you want?"
- tsurba 9mo agoMany discriminative models converge to same representation space up to a linear transformation. Makes sense that a linear transformation (like PCA) would be able to undo that transformation. https://arxiv.org/abs/2007.00810 https://arxiv.org/abs/2007.00810 Without properly reading the linked article, if thats all this is, not a particularly new result. Nevertheless this direction of proofs is imo at the core of understanding neural nets.
- Atlas667 9mo agoImagine collectively trying to recreate a human brain with semiconductors so capitalists can save money by not having to employ as many people
- tim333 9mo agoThere are other reasons beyond the employment thing. Understanding how the mind works maybe.
- Atlas667 9mo agoUnderstand the mind to then exploit it. Why else would they put so much money into something if not to try and get more out of it? Capitalists' morals are driven by their social position. To them this is right becauae its rewarding. To us its an akin abomination we create that destroys us But the problem isnt inherently tech. Its how society is structured around it that allows it to be used against us.
- YouAreWRONGtoo 9mo ago[dead]
- Simplita 9mo agoCurious if this connects with the sparse subnetwork work from last year. There might be an overlap in the underlying assumptions.
- augment_me 9mo agoI think the paper in general completely oversells the idea of "universality". For CNNs, the 'Universal Subspace' is simply the strong inductive bias (locality) forcing filters into standard signal processing shapes (Laplacian/Gabor) regardless of the data. Since CNNs are just a constrained subset of operations, this convergence is not that surprising. For Transformers, which lack these local constraints, the authors had to rely on fine-tuning (shared initialization) to find a subspace. This confirms that 'Universality' here is really just a mix of CNN geometric constraints and the stability of pre-training, rather than a discovered intrinsic property of learning.
- sigbottle 9mo agoFor me at least, I wasn't even under the impression that this was a possible research angle to begin with. Crazy stuff that people are trying, and very cool too!
- tempestn 9mo agoAfter reading the title I'm disappointed this isn't some new mind-bending theory about the relativistic nature of the universe.
- ycombigrator 9mo ago[dead]
- pmkary 9mo agoPlato's forms finally being proven...
- statusfailed 9mo agoI saw a similar (I think!) paper "Grassmannian Optimization Drives Generalization in Overparameterized DNN" at OPT-ML at neurips last week[0] This is a little outside my area, but I think the relevant part of that abstract is "Gradient-based optimization follows horizontal lifts across low-dimensional subspaces in the Grassmannian Gr(r, p), where r p is the rank of the Hessian at the optimum" I think this question is super interesting though: why can massively overparametrised models can still generalise? [0]: https://opt-ml.org/papers/2025/paper90.pdf https://opt-ml.org/papers/2025/paper90.pdf
- zkmon 9mo agoSo, while the standard models are like herbivores grazing on the internet data, they built a model that is a carnivore or a predator species trained on other models? Sounds like an evolution of the species.
- IAmBroom 9mo agoIf I can understand your metaphor, it's probably not sophisticated enough to be relevant. - I know what I do not know. -- I do not know AI.
- zirt 9mo ago[dead]
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- hagsdp00 9mo agoI have been trying to reproduce ("vibecoded" with some care) their results for the 500 loras part which I am familiar with and unfortunately can not see that drop at rank 16 that they show in their Figure and use for further claims. Looking forward to their code :)
- mlpro 9mo agoRead the paper end to end today. I think its the most outrageous ideas of 2025 - at least amongst the papers I've read. So counterintuitive initially and yet so intuitive. Personally, kinda hate the implications. But, a paper like this was definitely needed.