This paper reviews the development of the Receptance Weighted Key Value (RWKV) architecture, emphasizing its advancements in efficient language modeling. RWKV combines the training efficiency of Transformers with the inference efficiency of RNNs through a novel linear attention mechanism. We examine its core innovations, adaptations across various domains, and performance advantages over traditional models. The paper also discusses challenges and future directions for RWKV as a versatile architecture in deep learning.

翻译：本文回顾了接收加权键值（RWKV）架构的发展历程，重点阐述其在高效语言建模方面的进展。RWKV通过一种新颖的线性注意力机制，将Transformer的训练效率与RNN的推理效率相结合。我们分析了其核心创新、在不同领域的适应性调整，以及相较于传统模型的性能优势。本文还探讨了RWKV作为深度学习通用架构所面临的挑战与未来发展方向。