The 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.

i 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.

This 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.

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

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.

the T stands for tea :)

There'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.

You 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)

> 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.

thanks!