ReMamba: Equip Mamba with Effective Long-Sequence Modeling
Authors: name: Danlong Yuan, name: Jiahao Liu, name: Bei Li, name: Huishuai Zhang, name: Jingang Wang, name: Xunliang Cai, name: Dongyan Zhao

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.