Architecture-aware RL makes sliding-window attention stronger at math reasoning

A new arXiv paper argues that architecture-aware reinforcement learning can make sliding-window attention competitive in math reasoning.

Sliding-window attention is often attractive because it can reduce memory and compute demands, but it can struggle on tasks requiring longer context integration. The paper suggests targeted RL can help close that gap.

For model builders, the result is a reminder that architecture and post-training interact. A limitation that appears structural may be partly addressed through the right optimization setup.