刚刚，梁文锋署名，DeepSeek元旦新论文要开启架构新篇章

Core Insights - DeepSeek has introduced a new architecture called Manifold-Constrained Hyper-Connections (mHC) to address the instability issues in traditional hyper-connections during large-scale model training while maintaining significant performance gains [1][6][8]. Group 1: mHC Architecture - The mHC architecture extends the single residual flow of traditional Transformers into a multi-flow parallel structure, utilizing the Sinkhorn-Knopp algorithm to constrain the connection matrix on a doubly stochastic matrix manifold [1][8]. - The core objective of mHC is to retain the performance improvements from widening the residual flow while resolving training instability and excessive memory consumption [8][9]. - Empirical evidence shows that mHC not only addresses stability issues but also demonstrates exceptional scalability in large-scale training, such as with a 27 billion parameter model, where it only increased training time by 6.7% while achieving significant performance improvements [8][32]. Group 2: Challenges with Traditional Hyper-Connections - Traditional hyper-connections (HC) have led to severe training instability and limited scalability due to the fundamental disruption of the inherent identity mapping property, which is crucial for stable training [5][9]. - The widening of information channels in HC results in increased memory access overhead, contributing to what is known as the "memory wall" problem [9][5]. Group 3: Implementation and Efficiency - DeepSeek has designed a tailored infrastructure for mHC, which includes kernel fusion, selective recomputation, and an extended DualPipe communication overlap strategy to minimize memory usage and enhance efficiency [23][25][27]. - The Sinkhorn-Knopp algorithm is employed to ensure that the residual connection matrix remains stable and adheres to the properties of a doubly stochastic matrix, which helps mitigate gradient explosion issues [16][21]. Group 4: Experimental Validation - The research team conducted experiments using language model pre-training to validate the effectiveness of mHC, comparing it against baseline models and traditional HC [28][32]. - Results from various downstream benchmark tests indicate that mHC consistently outperforms baseline models and often surpasses HC, demonstrating its effectiveness in large-scale pre-training [34][33]. - The scalability experiments reveal that mHC maintains performance advantages even at higher computational budgets, showing only slight degradation in performance [36][37].