Transformers have revolutionized almost all natural language processing (NLP)
tasks but suffer from memory and computational complexity that scales
quadratically with sequence length. In contrast, recurrent neural networks
(RNNs) exhibit linear scaling in memory and computational requirements but
struggle to match the same performance as Transformers due to limitations in
parallelization and scalability. We propose a novel model architecture,
Receptance Weighted Key Value (RWKV), that combines the efficient
parallelizable training of Transformers with the efficient inference of RNNs.
Our approach leverages a linear attention mechanism and allows us to formulate
the model as either a Transformer or an RNN, which parallelizes computations
during training and maintains constant computational and memory complexity
during inference, leading to the first non-transformer architecture to be
scaled to tens of billions of parameters. Our experiments reveal that RWKV
performs on par with similarly sized Transformers, suggesting that future work
can leverage this architecture to create more efficient models. This work
presents a significant step towards reconciling the trade-offs between
computational efficiency and model performance in sequence processing tasks.

本文提出了一种名为 Receptance Weighted Key Value（RWKV）的新型模型体系结构，它将 Transformer 的并行训练与 RNN 的高效推理相结合，并利用了线性注意机制，使模型既可以被阐释为 Transformer，也可以被阐释为 RNN，从而在训练期间并行计算，并在推理期间保持计算和记忆的复杂度恒定，从而成为第一个可扩展到数十亿参数的非 Transformer 架构，实验表明，RWKV 的表现与同样大小的 Transformer 相当，在序列处理任务中为权衡计算效率和模型性能迈出了重要的一步。