arXiv:2603.17484v2 Announce Type: replace-cross Abstract: Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval. Continued pretraining on long-context data can help but is expensive due to the quadratic scaling of Attention. We observe that most tokens do not require (Global) Attention over the entire sequence and can rely on local context. Based on this, we propose L2A (Learning To Attend), a layer that enables conditional (token-wise) long-range memory access by deciding when to invoke global attention. We evaluate L2A on Qwe
The continuous push for more capable large language models necessitates overcoming architecture limitations related to context length and computational cost.
This research addresses a fundamental scaling challenge for LLMs, potentially unlocking new use cases requiring very long-context understanding without prohibitive compute costs.
The ability to process much longer sequences more efficiently could enable LLMs to tackle more complex, multi-document tasks and improve reasoning over extended narratives.
- · AI developers
- · Cloud providers
- · Data-intensive industries
- · Companies relying on short-context AI limitations
Conditional attention mechanisms reduce the computational burden of extending LLM context windows.
This efficiency gain could accelerate the development of more sophisticated AI agents capable of sustained, long-horizon tasks.
Improved long-context LLMs might enable new forms of automated analysis and synthesis that were previously infeasible due to context limitations.
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Read at arXiv cs.LG