[minimaltom]

> 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" ?

[yorwba]

What is classic about "skip updating parameters with high gradient/loss variance in multiple batches/samples"? Do you have a particular algorithm in mind that uses this heuristic?

[minimaltom]

Theres been multiple papers discussing how only updating parameters that have high agreement in update direction leads to less overfitting and better generalization. Lemme see if I can find em.

https://arxiv.org/abs/2411.16085 - set updates to 0 where theres disagreement in the sign of the parameter update - got accepted!

https://arxiv.org/pdf/2412.18052 - discard gradient updates from batches/minibatches that disagree where disagree means cosine distance threshold (they solved for 0.97 or something being optimal)