DeepSeek发布最新论文，破解大模型训练拥堵难题

Core Viewpoint - The DeepSeek team has introduced a new framework called "mHC" (Manifold-Constrained Hyper-Connections) that significantly improves the training performance of large-scale models by addressing issues related to the previous "HC" (Hyper-Connections) paradigm [1][4]. Group 1: Paper Overview - The paper focuses on the foundational aspect of large model training, specifically the residual connection paradigm, and proposes the mHC framework as a theoretical innovation to enhance model training stability [4][5]. - The mHC framework is likened to a smart traffic management system that regulates data flow in multi-lane connections, thereby increasing training stability and performance [5][6]. Group 2: Theoretical Innovation - The mHC framework builds upon the work of AI pioneers such as He Kaiming and ByteDance, who previously introduced the residual connection and HC paradigms, respectively [7][8]. - DeepSeek's contribution is positioned as an optimization of existing frameworks, aiming to reignite interest in macro-architecture design within the AI community [9]. Group 3: Company Strategy - Amidst a trend of commercialization in the large model sector, DeepSeek's focus on foundational model research underscores its strategic commitment to advancing basic model theory rather than immediate commercial applications [9].