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TinyLoRA – Learning to Reason in 13 Parameters
- measurablefunc 7mo agoWith four parameters I can fit an elephant, and with five I can make him wiggle his trunk so there is still room for improvement.
- esafak 7mo agoExcept learning to reason is a far cry from curve fitting. Our brains have more than five parameters.
- est 7mo agoreasoning capability might just be some specific combinations of mirror neurons. even some advanced math usually evolves applying patterns found elsewhere into new topics
- ekuck 7mo agospeak for yourself!
- voxelghost 7mo agoAfter a quick content browse, my understanding is this is more like with a very compressed diff vector, applied to a multi billion parameter model, the models could be 'retrained' to reason (score) better on a specific topic , e.g. math was used in the paper
- measurablefunc 7mo agoI agree, I don't think gradient descent is going to work in the long run for the kind of luxurious & automated communist utopia the technocrats are promising everyone.
- sdenton4 7mo agoIt's the statistics equivalent of 'no one needs more than 640kb of RAM'
- nativeit 6mo agoMy very first PC was a Packard Bell with 640KB of RAM. If I’d known, I’d have saved all my RAM for retirement…
- deleted 7mo ago[deleted]
- ValveFan6969 7mo ago[dead]
- a-t-c-g 7mo agoThe quality of custom models trained with proper reasoning datasets[0] even with small parameters (3-7B is sweet spot) is incredible now [0]: cartesien.io or Salesforce's WebscaleRL
- objektif 7mo agoWhat are you basing how good they are on? Personal experience or some benchmarks?
- a-t-c-g 7mo agoBenchmarks, we have internal ones testing reasoning fine-tuned v/s frontier + prompts For some use cases it can be parity performance at 1/20th the cost up to exceeds at 1/10th the cost. Trade-off is ofc narrow applicability
- objektif 6mo agoHow can I learn more about these models? Are they open source?
- a-t-c-g 6mo agothere are plenty of OSS finetuned models + base models around. If you're looking for doing these on your own dataset, worth getting in touch with cartesien.io or wire up https://github.com/SalesforceAIResearch/PretrainRL-pipeline https://github.com/SalesforceAIResearch/PretrainRL-pipeline
- objektif 6mo agoThank you.
- Sim-In-Silico 7mo ago[dead]
- matt123456789 7mo agoSuch low dimensionality of the LoRA vector must surely result in a close-to-linear modification to the KV calculation. This seems to me to imply that what we call "reasoning" is latent within the model. Pretty clear I didn't read the paper, I'm sure the authors address this.
- a-t-c-g 7mo agoYes - some degree of reasoning appears to be latent in the structure of language itself. But models trained explicitly on reasoning-focused data still perform better than models trained only on general corpora.* *At least up to 300B parameters, based on the models we’ve tested.
- crawfordcomeaux 6mo agoI wonder what the relationships between the grammar of a language, what it can compute, how it encodes, and what the minimal parameters/structure for reasoning looks like...
- a-t-c-g 6mo agonatural language may provide part of the scaffolding for reasoning, but the capability itself seems to depend more on learned transformations over internal representations than on language alone refs: https://arxiv.org/abs/2412.17819 https://arxiv.org/abs/2412.17819 https://arxiv.org/abs/2412.06769 https://arxiv.org/abs/2412.06769
- ValveFan6969 7mo ago[dead]
- kgeist 7mo ago>One theory is that the knowledge required to solve the task is already stored in the parameters of the model, and only the style has to change for task success >In particular, learning to generate longer outputs may be possible in few parameters Reminded me of: https://arxiv.org/abs/2501.19393 https://arxiv.org/abs/2501.19393 >we develop budget forcing to control test-time compute by forcefully terminating the model’s thinking process or lengthening it by appending “Wait” multiple times to the model’s generation when it tries to end. This can lead the model to double-check its answer, often fixing incorrect reasoning steps Maybe, indeed, the model simply learns to insert the EOS token (or similar) later, and the capability is already in the base model
- dollo_7 7mo agoNot sure if I buy it. First, SVD decomposition to obtain U, Σ, V is computationally expensive, so it would work only if we are not finetuning very big models. But my real concern comes at the results. The "13 parameters" looks like bait, because it is one result of finetuning a model on a very simple math benchmark, grade-school-math (GSM8K), an already very saturated benchmark on every model. Besides, it seems to happen only for the qwen family model... It looks like GSM8K was part of the training set of the qwen model, and this tinylora finetuning did the last adjustments to perfectly reflect that overtraining.
- robrenaud 7mo agoYeah, my big problem with the paper is it just might be an artifact of qwen's training process.
- taneq 6mo agoIn all fairness most of the unique stuff I can do is probably an artifact of my training process, so it seems unfair to deny an LLM the same accomodation.
- nativeit 6mo agoHow much did your training cost society?
- msdz 6mo agoThis got me thinking, and it might actually even be a comparable amount. Let's estimate 12 years of schooling run at minimum $100,000 per student, at least in the US [1], and then add onto that number whatever else you may do after that, i.e. a bunch more money if paid (college) or "unpaid" (self-taught skills and improvements) education, and then the likely biggest portion for white-collar workers, yet hard-to-quantify, in experience and "value" professional work will equip one with. Now divide the average SOTA LLM's training cost (or a guess, since these numbers aren't always published as far as I'm aware) by the number of users, or if you wanted to be more strict, the number of people it's proven to be useful for (what else would training be for), and it might not be so far off anymore? Of course, whether it makes sense to divide and spread out the LLMs' costs across users in order to calculate an "average utility" is debatable. [1] https://www.publicschoolreview.com/average-spending-student-stats/national-data https://www.publicschoolreview.com/average-spending-student-...
- sachaa 7mo agoIf 13 parameters can unlock better reasoning, then we will not be "training" models, we'll be steering them. Most of the capability is already there. The real unlock isn’t TinyLoRA, it’s what this implies: ultra-cheap, continuous adaptation. The bottleneck shifts from compute to having a good reward signal.
- MASNeo 7mo agoIs it an Aprils Fools publication?
- darkxanthos 6mo agoThis hit me too hard.
- Xx_crazy420_xX 7mo agoIf i understand it correctly, the analogy could be: Let's say we have a low level programmer expert and we try to teach him algebra either we: - (SFT): give him algebra book with new nomenclature, definitions, syntax - (RL): let him learn algebra using C syntax
- nathan_compton 6mo agoI don't think so. Fine tuning works on an input/output basis. You are rewarded for producing a plausible output _now_. RL rewards you later for producing the right output now. So you have to learn to generate a lot of activity but you are only rewarded if you end up at the right place. In SFT you are rewarded for generating tokens plausible to the proof, one token at a time. In RL you are expected to generate an entire proof and then you are rewarded or punished only when the proof is done.
- evermore611 7mo ago[dead]
- vasco 6mo agoMost data in the training set of most reasoning models is crap I guess.
- volume_tech 6mo ago[flagged]
- voxelghost 6mo agoThats a wonderful explanation (and roughly the conclussion I arrived at after browsing the paper), I just wish it would have been in the original post.
- kashifr 6mo agoYou can try out TinyLoRA in PEFT main now: https://huggingface.co/docs/peft/main/en/package_reference/tinylora https://huggingface.co/docs/peft/main/en/package_reference/t...
- cestith 6mo agoThis is interesting and all, but “LoRA” is painfully close to “LoRa” (which is related to radio networking, not AI) when just scanning a list of topics. We’re never going to beat the Shannon limit on acronyms and initialisms. I’m glad the rest of the anchor text gave some context.
- jorlow 6mo agoI see this comment on every single LoRA post despite the vast majority of posts being about LoRA not LoRa. Can we please stop beating this dead horse?
- ticulatedspline 6mo agoNever heard of the radio thing. I suspect LoRA has already eclipsed LoRa in general usage. It's probably more appropriate to complain on a LoRa post that it's too close to LoRA.
- HeyLaughingBoy 6mo agoAnd I'm the exact opposite. I never heard about LoRA, but I have used LoRa and was curious to see what it had to do with reasoning. It's just an unfortunate name collision: disambiguating by use of capitals only works with computers.
- sorenjan 6mo agoA version of this comment is posted in all submissions about Low Rank Adapters. I don't see how "Learning to reason in 13 parameters" would apply to low power radio communication, so it's even less relevant this time. > Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- cestith 6mo ago> I’m glad the rest of the anchor text gave some context. I’m sorry if that reads like a complaint.
- ashater 6mo agoLikely reasoning is part of the original model. It is well known that it is not possible to get a 1bn parameter model to reason, even with RL.
- nekusar 6mo agoCan a model that small dynamically grow? In other words, can it train itself AS it progresses through the network?
- 5555watch 6mo agoIt's not "13 parameters to reason", they just rotated the full 8B parameter space in 13 dimensions and found a rotation that was still able to reason. Depending on the latent structure, it's possible a nice rotation that would be perfect for some one specific problem, but you still got to search for it, and it's not a guarantee to exist. But it's a nice step towards LLM parameter-space interpretability.
- AndrewB234 6mo ago[flagged]