arXiv:2511.20102v3 Announce Type: replace Abstract: Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse attention to full-attention-trained models causes performance degradation due to train-inference distribution mismatch, and (2) a capability gap, where models trained purely with sparse attention lack complete gradient flow, preventing them from matching full-attention performance. We propose SSA (Sparse Sparse Attention), a training framework that integrates both sparse and full attention with bidirec
The continuous push for more efficient and scalable AI models makes advances in attention mechanisms highly relevant, as their quadratic complexity has been a known bottleneck.
This development could significantly improve the training and inference efficiency of large language models, making advanced AI more accessible and performant.
The proposed SSA framework tackles key limitations of sparse attention, potentially enabling more powerful and cost-effective AI development without the traditional performance trade-offs.
- · AI developers
- · Cloud computing providers
- · Large language model companies
- · Inefficient AI training methods
- · Hardware providers unprepared for increased demand
Reduced computational costs for training and operating large AI models.
Faster development cycles and deployment of increasingly complex AI applications across various industries.
Accelerated progress towards general AI capabilities due to more efficient model scaling and iteration.
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