> Second-order optimizers and natural gradient methods
Do second order optimizers help improve data efficiency? I assumed they’d help you get to the same minimum faster (but this is way outside my wheelhouse).
yes! typically the optimizer that trains faster also get better data efficiency. it maybe not be absolutely true, but that has been my observation so far. also see https://arxiv.org/pdf/2510.09378 for second-order methods.
Fundamentally I don't believe second-order methods get better data efficiency by itself, but changes to the optimizer can because the convergence behavior changes. ML theory lags behind the results in practice.
There was this very interesting paper out of Stanford this last September about pretraining under the unlimited compute but limited data paradigm[0]. Pretty much exactly the same thing but with ~200M training tokens instead.
I see you already mention diffusion - iirc there was a result not too long ago that diffusion models keep improving with more epochs for longer than AR models do.
diffusion is promising, but still an open question how much data efficient they are compared to AR. in practice, you can also train AR forever with high enough regularization, so let's see.
Curious about the baseline choice. modded-nanogpt was optimized for wall-clock speed, not data efficiency, so it seems like an unusual reference point for this kind of benchmark. Why not vanilla NanoGPT?
Modded-nanogpt is also much more data efficient than vanilla napogpt, even if some of the individual optimizations trade off higher throughput for worse data efficiency.
yes, agreed, modded-nanogpt is already a data-efficient variant of original nanogpt. just that the kinds of algorithms it allows are somewhat constrained because it optimizes for wall clock time.
Very cool idea. Interested to see how this progresses.
One question: how worried are you about over-training on this particular dataset? i.e. instead of generalizing you lean more toward memorization? Obviously you leave out a validation set but since you're meta-optimizing the model itself by its performance on the validation dataset you're still at risk of over-fitting.
yes, good point. right now, it's somewhat hard to overfit because the meta-optimization extracts tiny bits of information. but over time, we will switch the validation set to some other random subset of the FineWeb or even entirely OOD datasets!
I like the idea of flipping the constraint. Most ML benchmarks assume unlimited data and limited compute, so people optimize for speed.
If high-quality training data becomes the real bottleneck, then the interesting question is how much signal you can extract from the same dataset when compute is cheap.
This looks awesome!!! I’m curious on the ensemble: does it mean “train 8 different models and pick the best one”? That’s what my mind jumps to, but that also seems wrong, because I assume we could just keep increasing the number of different models you train to get a win.
hey, it's Samip (behind the Slowrun repo). yeah that's a fair point, we will mention them in the blog. but there are a couple of major differences:
1. our emphasis is on using more compute to get better data efficiency. this is important because there are lots of hacky chances that will get lower loss, but when compared to general methods that leverage a lot of compute, they don't do so well. and you can already see how this emphasis on compute leads to different methods to BabyLM!
2. our reasoning behind the repo is not anything to do with how much data a child sees. and our dataset is not tailored towards that either. it's simple pretraining on random subset of the internet. we know there are better training algorithms that get lower loss on that data, and we are finding those.
It seems like best etiquette would be to have a username with "bot" in it and include something in the post explicitly indicating it's a bot (e.g. a signature).
This isn't even a new problem where a good cultural solution hasn't been figured out yet. Reddit has had bot etiquette for years.
> Second-order optimizers and natural gradient methods
Do second order optimizers help improve data efficiency? I assumed they’d help you get to the same minimum faster (but this is way outside my wheelhouse).
[0] https://www.alphaxiv.org/abs/2509.14786
Still, just for reference, here's the paper I remembered: https://arxiv.org/pdf/2507.15857
If high-quality training data becomes the real bottleneck, then the interesting question is how much signal you can extract from the same dataset when compute is cheap.
This isn't even a new problem where a good cultural solution hasn't been figured out yet. Reddit has had bot etiquette for years.