# In Search of Goodness: Large Scale Benchmarking of Goodness Functions for the Forward-Forward Algorithm

Source: [In Search of Goodness: Large Scale Benchmarking of Goodness Functions for the Forward-Forward Algorithm](<https://www.sota2.com/research/paper/6925381bd6b0dd334292f4aa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-26T05:04:09.144Z

Authors: Arya Shah, Vaibhav Tripathi

Published: 2025-11-23T18:24:35.000Z

## Abstract

The Forward-Forward (FF) algorithm offers a biologically plausible alternative to backpropagation, enabling neural networks to learn through local updates. However, FF's efficacy relies heavily on the definition of "goodness", which is a scalar measure of neural activity. While current implementations predominantly utilize a simple sum-of-squares metric, it remains unclear if this default choice is optimal. To address this, we benchmarked 21 distinct goodness functions across four standard image datasets (MNIST, FashionMNIST, CIFAR-10, STL-10), evaluating classification accuracy, energy consumption, and carbon footprint. We found that certain alternative goodness functions inspired from various domains significantly outperform the standard baseline. Specifically, \\texttt{game\\\_theoretic\\\_local} achieved 97.15\\% accuracy on MNIST, \\texttt{softmax\\\_energy\\\_margin\\\_local} reached 82.84\\% on FashionMNIST, and \\texttt{triplet\\\_margin\\\_local} attained 37.69\\% on STL-10. Furthermore, we observed substantial variability in computational efficiency, highlighting a critical trade-off between predictive performance and environmental cost. These findings demonstrate that the goodness function is a pivotal hyperparameter in FF design. We release our code on \\href{https://github.com/aryashah2k/In-Search-of-Goodness}{Github} for reference and reproducibility.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on Fashion-MNIST 4x2000 (test)](<https://www.sota2.com/research/sota/image-classification-on-fashion-mnist-4x2000-test.md>): Image Classification; Fashion-MNIST 4x2000 (test); Accuracy: 68.72.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.18567>)

[PDF](<https://arxiv.org/pdf/2511.18567.pdf>)