arXiv:2602.18196v4 Announce Type: replace Abstract: Structured dilated attention has an appealing inference-time efficiency knob: it reduces the FLOPs of attention and the KV cache size by a factor of the dilation size D, while preserving long-range connectivity. While prior work studies it by training each configuration from scratch, directly sparsifying a pretrained attention model into a dilated pattern leads to severe accuracy degradation, preventing flexible reuse across inference scenarios. We introduce RAT+, a dense-pretraining architecture that augments attention with full-sequence rec
The continuous growth in demand for large AI models necessitates more efficient architectures to manage computational complexity and memory constraints.
This development offers a method to significantly improve the inference efficiency of AI models, making advanced AI more deployable and scalable in real-world applications.
The ability to train dense and infer sparse attention models will reduce the computational footprint and memory requirements of large AI models at inference time without sacrificing accuracy.
- · AI service providers
- · Cloud computing platforms
- · Hardware manufacturers (GPUs, TPUs)
- · AI/ML researchers
- · Inefficient cloud resource consumers
- · Companies unable to adapt to optimized AI architectures
Wider adoption and lower operational costs for large language models and other attention-based AI systems.
Accelerated development of more complex and capable AI agents due to reduced inference overhead.
Increased accessibility and democratization of advanced AI capabilities, potentially leading to new applications and markets.
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Read at arXiv cs.LG