Sliding Window Attention beats linear attention at zero cost in post-training
helloiamleonie · x · 2026-08-31
This post explains Sliding Window Attention (SWA), a local attention pattern that restricts tokens to attending only within a fixed-size neighborhood to reduce memory for long-context models. Citing new research, it claims that switching to a sliding-window attention mask with attention sinks post-training beats linear attention at no additional cost, challenging the complexity of alternative methods.
More from Research
- Nature paper uses language models to map single neurons behind human language — raphaelmilliere · 2026-10-16
- Reviewer shares workflow: feeding the finished review — never the paper — to AI for a second pass — StingMeleoron · 2026-10-16
- 3SUM record falls to O(n^1.921), Lean-verified — but will it yield usable algorithms? — iskander · 2026-10-16
- Ablation studies to help Kaggle Gemma 4 competition participants — ayushthakur0 · 2026-10-16
- Experts put global AI catastrophe risk before 2100 at 5%, LEAP survey finds — ben_j_todd · 2026-10-16
- Historical lesson for AI: tractors automated farm labor, teen boys stayed in school longer — soumitrashukla9 · 2026-10-16