Sakana AI researchers Jeffrey Seely and Julian Gould introduce Augmented Lagrangian Predictive Coding (PC-ALM), a local-learning alternative to backpropagation. By attaching a Lagrange multiplier to each layer constraint, PC-ALM keeps predictive coding's layer-local updates while recovering exact backprop gradients in linear networks. It matches BP across widths and depths from 8 to 128 at an inference budget of T = 2L, lifts gradient cosine to BP from 0.604 to 0.909 in the reference cell, and trains 1000-layer residual MLPs within about 2 points of backprop on MNIST. MIT-licensed JAX code is available. The post Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks appeared first on MarkTechPost .
Strategic AI Brief
Impact Analysis
Sakana AI researchers Jeffrey Seely and Julian Gould introduce Augmented Lagrangian Predictive Coding (PC-ALM), a local-learning alternative to backpropagation.
Market Signal
By attaching a Lagrange multiplier to each layer constraint, PC-ALM keeps predictive coding's layer-local updates while recovering exact backprop gradients in linear networks.
Tactical Warning
It matches BP across widths and depths from 8 to 128 at an inference budget of T = 2L, lifts gradient cosine to BP from 0.604 to 0.909 in the reference cell, and trains 1000-layer residual MLPs within about 2 points of backprop on MNIST. MIT-licensed JAX code is available. The post Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks appeared first on MarkTechPost .