arXiv:2605.14589v2 Announce Type: replace Abstract: Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make long-context adaptation expensive and difficult to reproduce. We propose EndPrompt, a method that achieves effective context extension using only short training sequences. The core insight is that exposing a model to long-range relative positional distances does not require constructing full-length inputs: we preserve the original short context as an intact first segm
The paper 'EndPrompt' addresses the critical challenge of extending large language models' context windows without incurring prohibitive computational costs, a bottleneck for current AI development.
This development allows for more efficient and cost-effective scaling of AI models, which can accelerate the deployment of more capable AI agents and systems.
The prior necessity of training on extremely long sequences for context extension is diminished, opening doors for broader model development and accessibility.
- · AI developers
- · Cloud providers
- · SaaS companies
- · Research institutions
- · Companies heavily invested in brute-force long-context training methods
More powerful and versatile LLMs can be developed and integrated into various applications at lower costs.
This could accelerate the creation of advanced AI agents capable of handling complex, multi-step tasks requiring extensive context.
Increased model capabilities and lower development barriers may lead to wider AI adoption across industries, potentially intensifying competition.
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Read at arXiv cs.CL