Appendix

We first provide additional elements to corroborate our findings: alignment measurement (Section A), and shallow baselines (Section B). We then discuss the process of adapting the considered architectures for DFA (Section C), and the issue of weight transport in attention layers (Section D). We provide some supplementary results for NeRF (Section E), including details of performance on each scene of each datatset, and a discussion on possible mitigation of DFA shortcomings. Finally, we outline steps necessary for reproduction of this work (Section F). Alignment measurement In feedback alignment methods, the forward weights learn to align with the random backward weights, making the delivered updates useful.