Hierarchically gated linear RNN (HGRN,Qin et al. 2023) has demonstrated
competitive training speed and performance in language modeling, while offering
efficient inference. However, the recurrent state size of HGRN remains
relatively small, which limits its expressiveness.To address this issue,
inspired by linear attention, we introduce a simple outer-product-based state
expansion mechanism so that the recurrent state size can be significantly
enlarged without introducing any additional parameters. The linear attention
form also allows for hardware-efficient training.Our extensive experiments
verify the advantage of HGRN2 over HGRN1 in language modeling, image
classification, and Long Range Arena.Our largest 3B HGRN2 model slightly
outperforms Mamba and LLaMa Architecture Transformer for language modeling in a
controlled experiment setting; and performs competitively with many open-source
3B models in downstream evaluation while using much fewer total training
tokens.

HGRN2 通过引入基于外积的状态扩展机制，在不引入额外参数的情况下，显著增加了循环状态大小，提高了语言建模、图像分类和长距离竞技场方面的性能。