Depth-Staggered Fibonacci Spacing for Sparse Attention: Static Schedules Beat Learned Dilation and Extrapolate Where Dense Attention Fails
arXiv:2606.28560v1 Announce Type: cross Abstract: We study sparse self-attention in which each query attends to a dense local window plus a set of Fibonacci-spaced offsets, with a per-layer scalar alpha that compresses or expands the spacing. Across 21 language models trained under one matched recipe (60M parameters, 512 hidden, 16 layers, 426M tokens), we compare four ways of setting alpha across depth: fixed, per-layer learned, a static linear stagger, and a coprime (anti-gridding) reassignment of that stagger, together with a reach-matched power-of-2 control. Three results stand out. First,
The continuous drive for more efficient and performant AI models, especially in transformers, necessitates innovation in core components like attention mechanisms.
Improved sparse attention techniques can significantly reduce compute requirements for large language models, making advanced AI more accessible and scalable.
This research suggests that static, carefully designed sparse attention patterns can outperform learned ones, offering a more predictable and potentially resource-efficient path to scaling transformers.
- · AI researchers
- · Cloud providers
- · Developers of large language models
- · Hardware manufacturers (indirectly, through higher utilization)
- · Inefficient sparse attention methods
- · Those heavily invested in a purely learned-dilation approach
More efficient and scalable large language models become feasible due to reduced computational overhead.
The cost of training and deploying advanced AI models could decrease, broadening their application and adoption.
Increased accessibility might accelerate AI innovation and democratize access to powerful AI capabilities beyond top-tier labs.
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