Hyper-Connections
Overview
Hyper-connections generalize a standard residual skip by maintaining several parallel residual streams and learning how layers read from and mix them, adding a route to capacity beyond width or depth. Common designs include token-dependent, manifold-constrained mixers—often doubly stochastic—as well as orthogonal or spectrally constrained transforms, sparse updates, and tensorized low-rank parameterizations. They are studied in language-model pretraining and finetuning, as well as vision, diffusion, and speech tasks; researchers also use stream ablations and probes to understand how the connections affect model behavior. The central tension is that richer routing can improve performance and parameter efficiency, yet streams may collapse or remain near identity, while constraints that stabilize gradients can limit expressivity or add computational cost.
Latest papers
18 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.