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NanoGPT Slowrun: 10x Data Efficiency with Infinite Compute
- yorwba 6mo agoRelated: Discussion on the initial NanoGPT Slowrun announcement: https://news.ycombinator.com/item?id=47251259 https://news.ycombinator.com/item?id=47251259 (185 points 15 days ago, 39 comments)
- sdpmas 6mo agothanks!
- littlestymaar 6mo ago> Data efficiency matters because compute grows much faster than data [2] (referencing a paper from 2022) I'm not convinced this is particularly true in today's world, if you have more compute, you can simply generate more, and higher quality, artificial data. That's what all labs have been doing since at least 2023. Also, the post references the Chinchilla-optimal training as a comparison baseline, but everyone has moved far beyond Chinchilla scaling, small models are routinely trained on 10-400 times more data than (1-40T tokens) than the Chinchilla-optimal number, so the entire industry went the complete opposite of what they are proposing. That doesn't mean the techniques presented here are useless or anything (I'm not qualified to judge) but you should take the introduction with a grain of salt.
- sdpmas 6mo ago> you can simply generate more, and higher quality, artificial data this is simply not true. and it's very clear if you look at continual learning, robotics, biology, etc. each has enough economic incentives to spend 1000x compute if that led to much better results, but we just don't know how to do that. good point on chinchilla, but our models are still absurdly large no matter what standards you compare them to.
- littlestymaar 6mo ago> this is simply not true. and it's very clear if you look at continual learning, robotics, biology, etc. each has enough economic incentives to spend 1000x compute if that led to much better results, but we just don't know how to do that I'm (and so is the post itself) talking about LLMs in particular, and this is indeed true for LLM.
- sdpmas 6mo agocontinual learning is LLMs :) ultimately everything will be/already is data bottlenecked.
- akshayvegesna 6mo agoYou seem to be making two points: - synthetic data is a valuable direction to pursue when you have compute - chinchilla scaling laws have some flaws for small models Both of these are side points to the core purpose of the Slowrun. The main point is the 100M tokens we train on push people to come up with novel ideas to improve pretraining, outside of facile synthetic data generation. I think we should continue to push on synthetic data, but why not come up with some new ideas too? You cannot use synthetic data for everything (see sdpmas's point)
- ACCount37 6mo agoThere's "cheap" bulk data - simple synthetics, unfiltered scrapes. Used for pre-training, especially early pre-training. And then there's "expensive" data. Human domain expert solutions, made by people you hire for $100 an hour. Used for SFT. For "expensive" data, it makes a lot of sense to use every trick in the book to squeeze that data for all its worth.
- ColonelPhantom 6mo agoIf generating synthetic data is such a great way to improve performance, why would it not be applied to the slowrun? Especially for the unlimited compute track, you should have plenty of time to generate as much synthetic data as your heart desires. Intuitively, I would expect the synthetic data to mostly just "regurgitate" the existing data, and not add much. But I could be wrong of course, and perhaps doing reinforcement learning somewhere could solve that issue as well (though I don't know if there is much hidden in FineWeb that you could RL on; at best you can do self-verification probably?)
- ACCount37 6mo agoThere's some evidence that carefully chosen synthetics might convey useful priors, improving convergence speed, generalizaiton and final performance. Just the other day this was posted, for example: https://news.ycombinator.com/item?id=47388293 https://news.ycombinator.com/item?id=47388293
- ColonelPhantom 6mo agoInteresting; I was not aware of those "universal synthetics" but they make sense: a stronger reasoning base would make modeling tasks easier. Thanks for the link! Again, though, if those work I assume they will be used for the slowrun. Surely a few hundred LoC to generate data would not be considered cheating :)
- nsnzjznzbx 6mo agoWe will get to the point where you can quickly bootstrap i.e. an LLM can train a better LLM in a loop, leave it and it can really learn. Like learn learn. "Train yourself to solve this problem see OBJECTIVE.md"
- nine_k 6mo agoThis is the kind of runaway self-improving development that proponents of the singularity keep talking about. The problem is that training appears to be really slow and expensive. Some quality thinking is required to improve the training approach and the architecture before committing resources to training a new large model. And even the largest models are by now not nearly as good at quality thinking as the best humans.
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- AliEveryHour16 6mo ago[dead]
- myylogic 6mo ago[dead]
- aledevv 6mo ago[dead]
- abeppu 6mo agoIn their little algorithm box on Chain Distillation, they have at step 2b some expression that involves multiplying and dividing by `T`, and then they say "where α = 0.5, T = 1.0". I think someone during the copy-editing process told them this needed to look more complicated?
- sdpmas 6mo agothe T stands for tea :)
- naruhodo 6mo agoAh, so it's a source of randomness! Presumably 1.0 corresponds to a really hot cup of fresh tea.
- arjie 6mo agotl;dr it makes sense once you see there are hidden softmax in there; it's just the explicit formula written out and then applied with the common param value Bloody hell, I am so unfamiliar with ML notation: L = (1 - α) · CE(M_k(x), y) + α · T² · KL(M_k(x)/T ‖ M_{k-1}(x)/T) So CE is cross-entropy and KL is Kullback-Leibler, but then division by T is kind of silly there since it falls out of the KL formula. So considering the subject, this is probably the conversion from logits to probabilities as in Hinton's paper https://arxiv.org/pdf/1503.02531 https://arxiv.org/pdf/1503.02531 But that means there's a hidden softmax there not specified. Very terse, if so. And then the multiplication makes sense because he says: > Since the magnitudes of the gradients produced by the soft targets scale as 1/T2 it is important to multiply them by T2 when using both hard and soft targets. I guess to someone familiar with the field they obviously insert the softmax there and the division by T goes inside it but boy is it confusing if you're not familiar (and I am not familiar). Particularly because they're being so explicit about writing out the full loss formula just to set T to 1 in the end. That's all consistent. In writing out the formula for probabilities q_i from logits M_k(x)_i: q_i = exp(M_k(x)_i / T) / sum_j exp(M_k(x)_j / T) Hinton says > where T is a temperature that is normally set to 1. Using a higher value for T produces a softer probability distribution over classes. So the real formula is L = (1 - α) · CE(softmax(M_k(x)), y) + α · T² · KL(softmax(M_k(x)/T) ‖ softmax(M_{k-1}(x)/T)) And then they're using the usual form of setting T to 1. The reason they specify the full thing is just because that's the standard loss function, and it must be the case that people in this field frequently assume softmaxes where necessary to turn logits into probabilities. In this field this must be such a common operation that writing it out just hurts readability. I would guess one of them reading this would be like "yeah, obviously you softmax, you can't KL a vector of logits". Good question. I just sort of skipped over that when reading but what you said made me think about it.
- andai 6mo agoWhat's the human baseline? How many cats does a human need to see to learn what a cat is, vs an AI? Maybe not quite a fair comparison since my human brain has been "learning" for half a billion years before I was born. I wonder if there's an equivalent of that for AI. Evolving the architectures?
- sdpmas 6mo agoi think evolution meta-learns the architecture, hyperparams. some domain knowledge too (for ex, we all perceive the world as 3d) but not much. if you compare the text consumed by human vs AI (and i think this is fair b/c even with evolution text is a pretty recent invention for humans), the gap is many orders of magnitude.
- throwaway894345 6mo agoTangentially, some scientists think humans may have hardwiring for detecting snakes https://en.wikipedia.org/wiki/Snake_detection_theory https://en.wikipedia.org/wiki/Snake_detection_theory
- ainch 6mo agoThe human genome contains around 1.5GB of information and DeepSeek v3 weighs in at around 800GB, so it's a bit apples-to-oranges. As you say, what's been evolved over hundreds of millions of years is the learning apparatus and architecture, but we largely learn online from there (with some built-in behaviours like reflexes). It's a testament to the robustness of our brains that the overwhelming majority of humans learn pretty effectively. I suspect LLM training runs are substantially more volatile (as well as suffering from the obvious data efficiency issues). If you'd like an unsolicited recommendation, 'A Brief History of Intelligence' by Max Bennett is a good, accessible book on this topic. It explicitly draws parallels between the brain's evolution and modern AI.
- jack_pp 6mo agoAnd that same information contained in an LLM is a compression of how many terabytes of training data? Maybe in the future there will be models an order of magnitude smaller and still better performing. What I'm saying is you can't judge the data in the genome by purely counting the bytes of data.
- 1425curlz80 6mo ago[dead]
- naasking 6mo agoGreat project. On the matter of data efficiency and regularization, I'd love to see someone try scaling GrokAlign!
- pastescreenshot 6mo agoThe result is interesting, but the practical question for me is where the compute bill lands once you include both training and serving. If a fixed-data regime pushes you toward ensembles plus chain distillation, is the endgame “serve the ensemble”, or do you expect most of the gain can be compressed back into a single deployable model later? That seems like the difference between a neat scaling result and a generally usable recipe.
- phr4ts 6mo agoThe brain does optimization during sleep. Is that something llms can benefit from?
- sigmoid10 6mo agoSleeping moves your memories from your working memory in your neocortex to your long term memory in your hippocampus. If you were an LLM, sleeping would basically move the contents from your adaptive system/memory prompt to the underlying model weights. It's weird that noone has really done that yet, but I can understand why the big AI chat corpos don't do it: You'd have to store a new model with new weights for each user if you don't want to risk private info spilling to others. If you have a billion users, you simply cant do that (at least not without charging obscene amounts of money that would prevent you from having a billion users in the first place). Current LLM architectures that start with a clean slate for every conversation are really good for serving to billions of people via cloud GPUs, because they can all run the exact same model and get all their customization purely from the input. So if we ever get this, it'll probably be for smaller, local, open models.
- dr_hooo 6mo agoOn a much simpler level, llm frameworks could re-summarize their context to keep relevant, use-case-specific facts, cleanup and also organize long and short term memory on some local storage, etc. So kind of like sleep. I think these examples are low hanging fruit to improve the perceived intelligence of LLM systems (so probably they're already used somewhere).
- sigmoid10 6mo agoWe already have that for a while. It works to some degree, but context tokens simply don't offer the level of compression that model weights do. At least with current approaches that keep the context human-readable.
- QubridAI 6mo agoIt's an interesting connection to the GPU-autoresearch post; once agents have the real infrastructure, sandboxing isn't just optional anymore it becomes a bottleneck.
- NooneAtAll3 6mo agoI thought "data efficiency" meant same quality with less parameters instead it's more parameters with less training data... but I don't really see any quality control?
- naasking 6mo agoSeems like they're relying on the loss as a measure, at least for now.
- webagent255 6mo ago[dead]