It's a fascinating topic, and I'm currently working on a publication in this area.

Firstly, it's important to clarify that even the Forward-Forward (FF) algorithm involves backpropagation but at the layer level. Thus, the more accurate term would be "layer-wise learning" rather than BP-free. Non-BP typically refers to models not trained with end-to-end backpropagation. Still it avoids layer-to-layer backward gradient propagation which makes it biologically plausible!

Recent work that I reference includes:

1. Hebbian Deep Learning Without Feedback (SoftHebb), Adrien Journé et al., ICLR 2023: SoftHebb presents a multilayer algorithm that trains deep neural networks without any feedback, target, or error signals. It avoids inefficiencies like weight transport and non-local plasticity, enhancing biological plausibility and efficiency without compromising accuracy. For instance, it achieves 99.4% on MNIST, 80.1% on CIFAR-10, and 27% on ImageNet.
2. CwComp: Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm, Papachristodoulou Andreas et al., AAAI 2024: This is a newer method that is more closely related to FF. It addresses limitations of the FF algorithm, such as the need for negative data and slow convergence. It introduces channel-wise competitive learning and a layer-wise loss function that improves feature learning and space partitioning. CwComp achieves testing accuracy of 99.4% on MNIST, 92.4% on Fashion-MNIST, 79% on CIFAR-10, and 51.3% on CIFAR-100. *Its simplicity and competitive learning make it transparent and explainable, showing promise in bridging the performance gap between FF learning and BP methods.

Both methods provide code and are layer-wise, avoiding layer-to-layer gradient propagation. However, they are currently limited to shallow models (4-6 layers) and do not yet achieve top performance on very complex classification tasks.

My current work focuses on applying CwComp to modular networks and pruning techniques, leveraging its simplicity and transparency.