(1/n) Introducing Hyperball — an optimizer wrapper that keeps weight & update norm constant and lets you control the effective (angular) step size directly. Result: sustained speedups across scales + strong hyperparameter transfer.
Compute grows fast but pretraining corpora don’t, so data-constrained optimization matters!
We propose Training-Time Testing (T³): each step takes a trial step on half the batch, tests it on the other half, and updates with the meta-gradient.
It achieves SOTA on SlowRun!
Meet Inkling 2 Small! We’ve tweaked the architecture a bit since the last generation, adding attention residuals (with a q/k/v split), gated attention, dynamic attention sink, dynamic shortconv, value norm and more. Don’t ask me “linear attention wen"
Check out Toner! A general rule that preconditions the optimizer's update with each layer's input activations. Applied to Muon and AdamW as MuonT and AdamT, it speeds up LM pretraining, improving the critical batch size by ~2× over Muon.
Super cool work by @bremen79 and clean blog! For scale-invariant L, the weight norm is a free accumulator. If L is geodesically convex on a hemisphere centered at a minimizer, w ← w − ∇L(w/‖w‖) gives schedule-free GD with AdaGrad-style optimal regret.
chatgpt.com/s/t_6af2082e74…
See also Lyu et al., App. B
arxiv.org/abs/2206.07085