Sliding Window Attention: Efficient Long-Context Modeling

**Sliding Window Attention: Efficient Long-Context Modeling**

Introduction Modern language models struggle when input sequences become very long because traditional attention mechanisms scale quadratically with the sequence length. This makes them computationally expensive and memory-intensive. Sliding window attention is a practical solution to this problem. It limits how much of the sequence each token attends to by focusing only on a fixed-size local context, reducing both compute and memory requirements while still capturing meaningful dependencies. Instead of every token attending to every other token, sliding window attention allows each token to attend only to its nearby neighbors within a defined window...

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