AI BriefingDiffusionBlocks Reduces Memory Usage by 3-4x
Key point
Sakana AI's DiffusionBlocks technique, presented at ICLR 2026, significantly reduces memory usage during deep learning training.
Details
DiffusionBlocks, presented by Sakana AI at ICLR 2026, is a technique that reinterprets the forward pass of deep networks as the signal denoising process of a Diffusion model, enabling independent block-wise training of the network.
While existing end-to-end backpropagation methods require keeping the entire network in memory simultaneously, hitting resource limits, this technique significantly reduces memory requirements by isolating and training only one block at a time.
It achieves performance equivalent to end-to-end training across various models such as ViT, DiT, and LLM, and is expected to reduce overall memory usage—including weights, gradients, optimizers, and activations—by 3-4x when applied at scale.
Additionally, it improves parallel training efficiency, reduces communication costs, and contributes to enhancing Looped Transformer training and Diffusion model inference efficiency.
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