Forward-Forward Algorithm

FFR extends Forward-Forward algorithm to regression tasks with 73% memory reduction

A new arXiv preprint introduces FFR (Forward-Forward for Regression), the first framework to adapt Hinton's Forward-Forward algorithm—a biologically plausible, backpropagation-free training method—to regression problems. FFR introduces an ordinal competitive goodness function, a stratified ladder architecture, and hierarchical prediction with uncertainty estimation to handle continuous target spaces. Across five real-world regression benchmarks, FFR recovers 98.6% of backpropagation accuracy while reducing peak training memory to 27% of BP's at depth 8 and 8% at depth 32, with per-iteration time around 72% of BP's.