DeepSeek’s New Architecture Can Make AI Model Training More Efficient and Reliable
DeepSeek’s latest paper introduces Manifold-Constrained Hyper-Connections (mHC), a method designed to make large AI model training more stable and efficient by constraining residual signal flow. The architecture was tested on models up to 27 billion parameters and showed improved training stability without excessive overhead. Research focuses on reducing costly training failures rather than hardware power changes.
