arXiv:2512.21075v3 Announce Type: replace Abstract: Deep neural networks have achieved remarkable success in practice, yet a mechanistic understanding of how features evolve during training remains incomplete, especially in the large-depth limit. For ResNets under depth-$\mu$P scaling, prior work treats the layer index $\ell$ as a continuous time $t_\ell = \ell/L$, yielding SDE descriptions of the training dynamics. A key unresolved issue is that backpropagation reuses each forward weight matrix $W_\ell$ through its transpose $W_\ell^\top$, creating correlations between forward features and ba
Source: arXiv cs.LG — read the full report at the original publisher.