[2603.24790] Local learning for stable backpropagation-free neural network training towards physical learning

Computer Science > Machine Learning arXiv:2603.24790 (cs) [Submitted on 25 Mar 2026] Title:Local learning for stable backpropagation-free neural network training towards physical learning Authors:Yaqi Guo, Fabian Braun, Bastiaan Ketelaar, Stephanie Tan, Richard Norte, Siddhant Kumar View a PDF of the paper titled Local learning for stable backpropagation-free neural network training towards physical learning, by Yaqi Guo and 5 other authors View PDF HTML (experimental) Abstract:While backpropagation and automatic differentiation have driven deep learning's success, the physical limits of chip manufacturing and rising environmental costs of deep learning motivate alternative learning paradigms such as physical neural networks. However, most existing physical neural networks still rely on digital computing for training, largely because backpropagation and automatic differentiation are difficult to realize in physical systems. We introduce FFzero, a forward-only learning framework enabling stable neural network training without backpropagation or automatic differentiation. FFzero combines layer-wise local learning, prototype-based representations, and directional-derivative-based optimization through forward evaluations only. We show that local learning is effective under forward-only optimization, where backpropagation fails. FFzero generalizes to multilayer perceptron and convolutional neural networks across classification and regression. Using a simulated photonic neural n...