arXiv AI
Jun 11

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning

arXiv:2606. 11634v1 Announce Type: new Abstract: The rapid progress of reasoning and agentic large language models (LLMs) has increased the demand for long-context inference, but self-attention (SA) scales quadratically with context length.

By Kai Liu, Peijie Dong, Xinchen Xie, Jianfei Gao, Qipeng Guo, Xiaowen Chu, Shaoting Zhang, Kai Chen
arXiv AI
Jun 10

Blurry Window Attention

arXiv:2606. 09862v1 Announce Type: cross Abstract: The Softmax Attention operation in Transformer language models has a quadratic complexity in the sequence length and a growing state size in the form of KV cache, which becomes a bottleneck in long context scenarios.

By Axel Laborieux, Christos Sourmpis, Juan Gabriel Kostelec, Qinghai Guo