Sakana Labs published a result on May 29, summarized via an X post from David Ha, claiming a new training method for deep neural networks that breaks the network into independent blocks and trains each block locally, sidestepping the memory cost of holding the entire network during end-to-end backpropagation.
The technical claim is that the forward pass is treated like a diffusion model denoising a signal, which is what enables the block-wise local training. If the result holds at scale, the memory savings could be substantial: end-to-end backprop has been the binding constraint on training networks at the largest scales for a decade.
Two caveats are immediate. The announcement is a Twitter thread, not a peer-reviewed paper. Sakana Labs has previously published results that did not always reproduce cleanly outside their own lab. Independent replication on a standard benchmark is the verification step that matters here.
For frontier-scale training infrastructure teams, this is a result worth tracking but not yet a result worth re-architecting around. Watch for a paper, a code release, or an independent replication before reading it as a fundamental shift in how networks get trained.
Posted by David Ha on X on 2026-05-29.
