titleJet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE
authorsHaozhan Tang, Zerui Wang, Yuxian Gu, Song Han, Han Cai
date2026-07-08
venuearXiv (preprint, not peer reviewed)
idarXiv:2607.07740
affiliationNVIDIA
upvotes24 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractModern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces routinely push the input an order of magnitude past the pretraining window, making zero-shot context extension the dominant deployment path for open-weight checkpoints. Most existing zero-shot methods fix a single rescaling factor up front, so an aggressive factor sacrifices short-context fidelity while a conservative one breaks down at long contexts. We propose Jet-Long, a tuning-free zero-shot method that pairs a local RoPE-faithful window with a long-range window whose rescaling factor adapts dynamically to the current sequence length, recovering the base model exactly at short inputs while extrapolating cleanly at long ones. An inclusion-exclusion attention merge and an on-the-fly RoPE correction rotation make the bifocal construction essentially free at inference; fused into a single CuTe kernel, long-context prefill reaches up to 1.39times FA2 throughput on H100 (approaching the Hopper-only FA4), and single-batch generation incurs le 4% overhead at every length. On Qwen3-1.7B/4B/8B up to 128K context, Jet-Long leads RULER by +4.79/+2.18/+2.03~pp over the strongest baseline at 1.7B/4B/8B, achieves the best overall accuracy on HELMET-RAG (a benchmark identified by HELMET as the most efficient predictor of downstream long-context performance) and attains the lowest PG-19 perplexity. Jet-Long also generalizes to hybrid attention architectures such as Jet-Nemotron for further long-context improvement without retraining, and remains hyperparameter-resilient for ease of deployment.

cite

@misc{tang2026jet,
  title={Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE},
  author={Haozhan Tang and Zerui Wang and Yuxian Gu and Song Han and Han Cai},
  year={2026},
  eprint={2607.07740},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2607.07740}
}