RecycleGPT: An Autoregressive Language Model with Recyclable Module
Authors: name: Yufan Jiang, name: Qiaozhi He, name: Xiaomin Zhuang, name: Zhihua Wu, name: Kunpeng Wang, name: Wenlai Zhao, name: Guangwen Yang

Existing large language models have to run K times to generate a sequence of
K tokens. In this paper, we present RecycleGPT, a generative language model
with fast decoding speed by recycling pre-generated model states without
running the whole model in multiple steps. Our approach relies on the
observation that adjacent tokens in a sequence usually have strong correlations
and the next token in a sequence can be reasonably guessed or inferred based on
the preceding ones. Through theoretical evaluations and practical tests on
downstream text generation tasks, we demonstrate the effectiveness of our
approach in lowering inference latency, achieving up to 1.4x speedup while
preserving high performance.