SimpleHC Reduces Training Overhead in Hyper-Connection Architectures
November 3, 2026
Researchers identified that repeated access to the expanded residual state causes significant training overhead in Hyper-Connection (HC) architectures. SimpleHC addresses this by removing residual mixing and using shared memory access to maintain performance while improving efficiency.
HOW THIS AFFECTS YOU
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builderYou can train more efficient models by reducing the overhead associated with expanded residual states.
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researcherYou can simplify HC architectures by using learned gating without mixing to match mHC performance.