A Survey of RWKV

TL;DR

This paper provides the first comprehensive review of the RWKV model, highlighting its hybrid recurrent-attention architecture, efficiency in handling long sequences, and its applications across NLP and vision tasks.

Contribution

It systematically reviews RWKV's principles, compares it with Transformers, and discusses its applications, challenges, and future research directions.

Findings

RWKV captures long-range dependencies efficiently.

It reduces computational costs compared to Transformers.

RWKV performs well across multiple domains.

Abstract

The Receptance Weighted Key Value (RWKV) model offers a novel alternative to the Transformer architecture, merging the benefits of recurrent and attention-based systems. Unlike conventional Transformers, which depend heavily on self-attention, RWKV adeptly captures long-range dependencies with minimal computational demands. By utilizing a recurrent framework, RWKV addresses some computational inefficiencies found in Transformers, particularly in tasks with long sequences. RWKV has recently drawn considerable attention for its robust performance across multiple domains. Despite its growing popularity, no systematic review of the RWKV model exists. This paper seeks to fill this gap as the first comprehensive review of the RWKV architecture, its core principles, and its varied applications, such as natural language generation, natural language understanding, and computer vision. We assess how RWKV compares to traditional Transformer models, highlighting its capability to manage long sequences efficiently and lower computational costs. Furthermore, we explore the challenges RWKV encounters and propose potential directions for future research and advancement. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/RWKV-Survey.