arXiv:2604.20920v2 Announce Type: replace Abstract: Sparse attention can reduce the cost of long-context inference, but most variants introduce new architectural components. We introduce Simplified Sparse Attention (SSA), a simpler approach to sparse attention that requires no architectural changes. Concretely, we first perform continued pretraining on sequences interleaved with gist tokens. We optimize the standard next-token loss as usual, but the gist tokens use an attention mask to restrict what parts of the context the language model can attend to; this teaches the model to pack each chun
The continuous drive for more efficient and scalable AI models, particularly for long-context understanding, pushes for innovation in attention mechanisms.
Simplified Sparse Attention could significantly reduce the computational cost of large language models, making advanced AI more accessible and performant for longer inputs.
The ability to handle extended contexts in AI models becomes more feasible without the need for complex bespoke architectural changes, potentially accelerating AI development and deployment.
- · AI developers
- · Cloud providers
- · Businesses using long-context AI applications
- · Hardware manufacturers for inference
Reduced operational costs for AI inference, especially for demanding applications.
Democratization of advanced AI capabilities due to lower resource requirements.
Acceleration of complex AI agent development that relies on extensive contextual understanding.
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Read at arXiv cs.LG