3 papers
cs.LG2026
Depth Memory
David González-Martínez, Philipp Nazari, T. Konstantin Rusch +1
Residual connections propagate information through a network by additively accumulating layer outputs. Recently proposed alternatives, such as Manifold-Constrained Hyper-Connection…
cs.CV2026
DepthBench: Measuring How Residual Connections Enable More Computational Depth
Keyu Wang, Yangyi Huang, Jiale Kang +3
Depth is a natural way to increase the computational capacity in Transformers, yet the contribution of deeper layers can diminish as depth grows larger. Recent approaches enhance n…
cs.LG2026
ISO: An RLVR-Native Optimization Stack
Hanqing Zhu, Wenyan Cong, Zhizhou Sha +8
Reinforcement learning with verifiable rewards (RLVR) advances the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-s…