  Visual Language Models (VLMs) have rapidly progressed with the recent success
of large language models. However, there have been few attempts to incorporate
efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In
this study, we introduce VisualRWKV, the first application of a linear RNN
model to multimodal learning tasks, leveraging the pre-trained RWKV language
model. We propose a data-dependent recurrence and sandwich prompts to enhance
our modeling capabilities, along with a 2D image scanning mechanism to enrich
the processing of visual sequences. Extensive experiments demonstrate that
VisualRWKV achieves competitive performance compared to Transformer-based
models like LLaVA-1.5 on various benchmarks. To facilitate further research and
analysis, we have made the checkpoints and the associated code publicly
accessible at the following GitHub repository:
\href{https://github.com/howard-hou/VisualRWKV}{https://github.com/howard-hou/VisualRWKV}.
