ReMamba: Equip Mamba with Effective Long-Sequence Modeling
Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao
Abstract
While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence suggests its capacity to comprehend long contexts is limited compared to transformer-based models. In this study, we investigate the long-context efficiency issues of the Mamba models and propose ReMamba, which enhances Mamba’s ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. Experimental results on the LongBench and L-Eval benchmarks demonstrate ReMamba’s efficacy, improving over the baselines by 3.2 and 1.6 points, respectively, and attaining performance almost on par with same-size transformer models.
BibTeX
@inproceedings{emnlp2025_remambaequipmamb,
title = {ReMamba: Equip Mamba with Effective Long-Sequence Modeling},
author = {Danlong Yuan and Jiahao Liu and Bei Li and Huishuai Zhang and Jingang Wang and Xunliang Cai and Dongyan Zhao},
booktitle = {EMNLP 2025},
year = {2025}
}