Zixuan Liu, Nan Xue, Rui Bao, Yuhang Xu, Xiaofeng Cao, Jiangchao Yao, Zhiyong Chen
LoopAhead enhances decoding speed in looped Transformers by using a lightweight drafter to predict multiple future tokens from early layers, while deeper layers guide which drafts to pursue or pause. This approach reduces redundant computation and leverages the model's recurrent structure for efficient speculative generation. It achieves up to 7.82x faster decoding without altering the final output distribution.
Faster decoding enables real-time applications of large language models, such as interactive chatbots and live translation, without sacrificing accuracy. LoopAhead improves efficiency in parameter-constrained settings by better utilizing looped architectures, making high-performance inference more accessible.
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