ANCRe makes residual connections learnable, accelerating convergence in LLM pretraining, diffusion models, and deep ResNets at under 1% overhead

The paper revisits residual connections, the default mechanism for deepening neural networks, from an optimization perspective and provides rigorous analysis proving that the layout of residual connections can fundamentally shape convergence behavior and even induce an exponential gap in convergence rates. Whereas residual connections are usually treated as a fixed architectural default, this work elevates connection layout itself into an object of analysis that governs convergence properties, with a provable rate gap. The evidence is the rigorous analysis described in the abstract, i.e., a theoretical argument; the abstract does not give theorem numbers, assumptions, or the explicit rate expression.

It introduces ANCRe (adaptive neural connection reassignment), a principled and lightweight framework that parameterizes and learns residual connectivities from the data, adaptively reassigning residual connections. Unlike conventional fixed residual connections, the method lets the connection structure adapt to data rather than only deepening the network or adjusting weights. The abstract states negligible computational and memory overhead (<1%) but does not give the parameterization, learning objective, or optimization details.

In extensive numerical tests across large language model pretraining, diffusion models, and deep ResNets, ANCRe shows consistently accelerated convergence, boosted performance, and enhanced depth efficiency over conventional residual connections. The results extend the method from analysis to multiple model and task families, covering language modeling, generative modeling, and vision backbones. The evidence is numerical experiments across three model families, described as consistent in the abstract; no specific datasets, model scales, metric values, or ablation settings are provided.

The work targets training settings that need deeper networks, especially depth-sensitive architectures such as large language model pretraining, diffusion models, and deep ResNets; its value lies in trading under 1% computational and memory overhead for more effective use of depth, making it relevant to teams sensitive to training cost yet seeking gains from depth. Theoretically, the link between residual connection layout and convergence rates offers an analytical starting point for designing learnable connection structures; practically, it suggests treating connection topology as a trainable component rather than only tuning depth or width.

At the abstract level, the assumptions behind the rigorous analysis and the exact form of the exponential convergence-rate gap are not stated, nor are ANCRe's parameterization, learning objective, or training stability. Although the numerical tests cover large language model pretraining, diffusion models, and deep ResNets, specifics on datasets, model scales, evaluation metrics, and ablations are missing, so the magnitude and applicability boundary of the reported consistent acceleration, performance gains, and depth efficiency still need confirmation in the full text. In addition, the baseline and configuration against which the under 1% overhead is measured remain open questions for a careful reader.