Idk to me this is just redescribing what deep neural networks do without actually explaining why anything happens. I guess it "unifies" things but I am kinda over most unifying theories. Everything is Bayesian, everything is a graph or a group or some other fancy geometric structure, everything is a category. Ultimately the best framework is whatever is useful enough to explain what's happening in such a way that a practitioner can manipulate the model towards a desired outcome. In other words, where is the knob? The tool they share may be interesting and I hope to play with it to see what happens at different levels of noise applied to the labels.

This is a fascinating mathematical framework, but the post title might be a bit of an overreach. I often wonder if "a theory of deep learning" could exist that could be stated succinctly and that could predict (1) scaling laws and (2) the surprising reliability of gradient descent.

Note that I said "predict" not "describe". It feels like we're still in the era of Kepler, not Newton.

The relevant paper: "A Theory of Generalization in Deep Learning". https://arxiv.org/abs/2605.01172

> That is, if the batch signal on a parameter exceeds its leave-one-out noise, update it; if not, skip it. This is a one-line change to Adam that accelerates grokking by 5x, suppresses memorization in PINNs, and improves DPO fine-tuning, eliminating the need for validation sets entirely.

Does anyone understand the formula they expressed above this sentence? is this just the classic "skip updating parameters with high gradient/loss variance in multiple batches/samples" ?

This essay seems to be related to the paper "There Will Be a Scientific Theory of Deep Learning" [1] which was discussed here recently [2].

[1] https://arxiv.org/pdf/2604.21691

[2] https://news.ycombinator.com/item?id=47893779

Interesting read. I remember the grokking paper when it came out but I don't think I've ever seen that classic grokking loss curve in my own hands on real data. Curious if others have seen it more often in practice

A very fascinating read.

As a fellow tufte css enjoyer, Why is user select turned off on the sidenotes? I would like to be able to copy paste them quite badly.

What a beautifully written article. It's extremely that I favourite an article but this is one.

Does anyone happen to know what font this site is using? It looks really elegant.

This is a beautifully written way of saying “Some parts of what the network memorizes affect test behavior, and some don’t.” But that’s not a theory of deep learning, the grand unified theory would explain that.

We're given a signal channel and a reservoir. Signal lives in the channel, noise lives in the reservoir, and the reservoir supposedly doesn’t show up at test time.

Okay, but then we have: why would SGD put the right things in the right bucket?

If the answer is “because the reservoir is defined as the stuff that doesn’t transfer to test,” then this is close to circular.

The Borges/Lavoisier stuff is a tell. "We have unified the field” rhetoric should come after nontrivial predictions and results. Claiming to solve benign overfitting, double descent, grokking, implicit bias, risk of training on population, how to avoid a validation set, and last but not least, skipping training by analytically jumping to the end is 6 theory papers, 3 NeurIPS winners, and a $10B startup. Let's get some results before we tell everyone we unified the field. :) I hope you're right.

So, this is either the paper of the year, or ... definitely not the paper of the year.

https://arxiv.org/pdf/2605.01172 is the current version. The money graphs are page 8 and on where they show (some weirdly thick) line charts with loss results reached in roughly 1/5 the number of steps that Adam takes, just what the blog post mentions.

They also claim holding back test data is not needed, also with more graphs.

I'm not an ML scientist, and I did not attempt to seriously parse the math. It reads to me as something precisely in that liminal space some math papers do where there's enough new terminology that actually parsing through it all is going to take real, concerted effort, possibly with mild brain damage as a risk.

Their 3d graphs of "kernel eigenstructure" also do double duty for me as totally impenetrable and possibly part of an April fool's ML paper that's hilarious to insiders. Or maybe they show something really amazing; they definitely seem to converge into a shape...What does that shape mean??? Why??? What is an eigenstructure? Is it just 3D eigenvectors of some matrices? Is it natural to have a 3D shape representing these large matrices? If not, how and why were these projected down? And why are they different colors in the paper?? You get the feel for my level of understanding.

I think it would frankly just be easier to validate this claim than parse the whole paper. If only I could understand

  > Each one-step kernel increment ηKMtSS integrates into WMS , so a sequence of one-step rate-maximizers is the greedy policy whose integral is the signal-channel content of the trajectory through G, exactly as plain SGD is the greedy step whose integral is empirical-risk descent through D. The diagonal cutoff µ2 k >σ2 k/(b−1) is the optimal first-order preconditioner for population risk on any diagonal base, and a streaming variance EMAˆst of squared gradient deviations realizes it as a one-line change to AdamW: one extra parameter-sized state vector and a per parameter gate that multiplies the standard moment update

Also of note to me, they talk about grokking which I found SUUUPER fascinating when it was first reported, and have never heard about since. So I was really glad to read about it and read that there has been a little academic work on the phenomenon.

Finally, of the three models they repot results on, two are extremely tiny, the last is a DPO round on Qwen 0.5B -- if the code for that is published, I imagine it would be easy to adapt and evaluate in other regimes.