Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.
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Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant
Cumulative-goodness Forward-Forward networks exhibit layer free-riding where discrimination gradients decay exponentially with prior positive margins; per-block, hardness-gated, and depth-scaled remedies yield 4-45x better layer separation but <1% accuracy change on CIFAR and Tiny ImageNet.