arXiv:2509.10406v4 Announce Type: replace Abstract: Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs. We present Multipole Semantic Attention (MuSe), which accelerates 64k-context pretraining by 36% while matching baseline loss, requiring no architectural changes. MuSe clusters queries and keys separately in representation space. This yields query-specific summaries that substantially outperform spatial blocking at matched sparsity, while also enabling drop-in compatibility with existing pretrai
The continuous drive to scale AI models to longer contexts and larger datasets necessitates more efficient attention mechanisms to overcome computational bottlenecks.
This development addresses a critical scaling limitation for pretraining large language models, enabling faster and more cost-effective development of advanced AI.
Pretraining transformers on long sequences becomes significantly more efficient without requiring architectural changes, potentially accelerating AI development cycles.
- · AI model developers
- · Hyperscale cloud providers
- · AI research institutions
- · SaaS companies leveraging large AI models
- · Companies reliant on less efficient attention mechanisms
- · Hardware producers whose products are not optimized for new efficiency paradigms
Faster and cheaper pretraining of powerful AI models becomes possible.
This could lead to a proliferation of more capable AI models operating on larger contexts, potentially accelerating the development of AI agents.
Increased efficiency in AI training could further centralize AI development power among entities with vast compute resources but might also democratize access through reduced costs.
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Read at arXiv cs.LG