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This research addresses the critical issue of underutilized depth in modern foundation models by proposing ANCRe, a lightweight framework that adaptively reassigns residual connections to optimize convergence rates. By learning connectivity patterns directly from data, the method achieves significant performance gains with negligible overhead across large language and diffusion models. The work offers a principled approach to maximizing the efficiency of deep network architectures without relying on standard residual shortcuts.
ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling
arXiv:2602.09009v2 Announce Type: replace-cross Abstract: Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep layers are often underutilized. This paper revisits the default mechanism for deepening neural networks, namely residual connections, from an optimization perspective. Rigorous analysis proves that the layout of residual connections can fundamentally shape convergence behavior, and even induces an exponential gap in convergence rates. Prompted by this insight, we introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data. ANCRe adaptively reassigns residual connections with negligible computational and memory overhead ($<1\%$), while enabling more effective utilization of network depth. Extensive numerical tests across pre-training of large language models, diffusion models, and deep ResNets demonstrate consistently accelerated convergence, boosted performance, and enhanced depth efficiency over conventional residual connections.
Abstract:Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep layers are often underutilized. This paper revisits the default mechanism for deepening neural networks, namely residual connections, from an optimization perspective. Rigorous analysis proves that the layout of residual connections can fundamentally shape convergence behavior, and even induces an exponential gap in convergence rates. Prompted by this insight, we introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data. ANCRe adaptively reassigns residual connections with negligible computational and memory overhead ($<1\%$), while enabling more effective utilization of network depth. Extensive numerical tests across pre-training of large language models, diffusion models, and deep ResNets demonstrate consistently accelerated convergence, boosted performance, and enhanced depth efficiency over conventional residual connections.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.09009 [cs.LG]
(or arXiv:2602.09009v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.09009 Focus to learn more arXiv-issued DOI via DataCite
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