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Layer Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant

NeurIPS 2026 · Architectures and efficiency › CNN design · approach: Theory of convolutional inductive bias

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Formalizes "layer free-riding" in cumulative-goodness Forward-Forward networks, where later layers receive vanishing class-discrimination gradients as earlier layers already separate classes, and studies whether it can be repaired and whether repair improves accuracy.

Problem

Cumulative-goodness Forward-Forward (FF) training may leave later layers with a task that earlier layers have already partially solved, so their learning signal weakens.

Approach

Formalizes layer free-riding: under the softplus FF criterion, the class-discrimination gradient reaching block d decays exponentially with the positive margin accumulated by preceding layers; the paper shows this effect is real and repairable but not accuracy-dominant.

Key results

• The class-discrimination gradient reaching block d under softplus FF decays exponentially with the positive margin accumulated by preceding blocks (per the abstract).
• The free-riding effect is reported as real and repairable, but repair is not accuracy-dominant (per the abstract).

Surprising findings

• Repairing layer free-riding does not translate into a dominant accuracy gain.

Limitations

• Full text unavailable; limitations not stated in the abstract.

Experimental setup

Builds on: Forward-Forward algorithm

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