Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training

Name: Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training
Uploaded: 2026-10-08T20:42:29.966034+00:00
Duration: 1110 s
Description: The study tested Forward-Forward (FF) learning, a layer-local training method, against full backpropagation on real images. Findings show that synthetic benchmarks overestimate FF's performance because the gap between FF and backpropagation widens with class count on real data, revealing a ceiling in transferability not seen at smaller scales.

The study tested Forward-Forward (FF) learning, a layer-local training method, against full backpropagation on real images. Findings show that synthetic benchmarks overestimate FF's performance because the gap between FF and backpropagation widens with class count on real data, revealing a ceiling in transferability not seen at smaller scales.

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