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arXiv AI By Zixuan Liu, Nan Xue, Rui Bao, Yuhang Xu, Xiaofeng Cao, Jiangchao Yao, Zhiyong Chen

LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models

Read the original on arXiv AI →

LoopAhead is a speculative decoding framework designed for looped Transformers, which repeatedly apply the same Transformer layers to improve parameter efficiency. It introduces a lightweight lookahead drafter (LAD) that predicts multiple future tokens using early-loop states, while deeper-loop feedback determines which draft branches to continue or pause, and the full-depth model finalizes token decisions. This approach achieves up to 7.82× faster decoding than traditional autoregressive methods while maintaining the target model’s output distribution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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