Q

We are a research lab working on moonshots in deep learning. We are building new learning algorithms that achieve orders-of-magnitude gains in data efficiency and generalization by scaling to unimaginable amounts of compute, even with little data.

Our investors include Jeff Dean, among many others.

Solving generalization

Approximating Solomonoff Induction

Pretraining data efficiency

NanoGPT Slowrun

13x data efficiency with hyper-epoch pretraining

Scaling laws and new scaling axes

Computational depth is all you need:
towards 107-layer neural nets
(X)

Replacing backprop/gradient descent

Dust: Pretraining transformers
without backpropagation

research@qlabs.sh