AI BriefingA new deep network training method that goes beyond end-to-end backprop, taken for granted for over 10 years
Key point
It proposes a new method that dramatically reduces memory usage by dividing the network into blocks and training them independently.
Details
The existing end-to-end backprop approach requires keeping the entire network in memory at once, causing serious resource shortage problems during AI training.
A new study proposes a method that divides the network into multiple blocks and trains each independently. The key idea is to treat the network's forward pass like the process by which a diffusion model denoises a signal.
Through this approach, memory requirements were dramatically reduced, and performance equivalent to the end-to-end approach was achieved across various models such as ViT, DiT, and LLM. This research is scheduled to be presented at ICLR 2026.
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