← Back to all news
arXiv Machine Learning By Hyeongju Ha, Jae-Joon Kim

WaveFront Decoding: Parallelized Self-Speculative Decoding for Looped Language Models

Read the original on arXiv Machine Learning →

WaveFront Decoding (WFD) is a training‑free, self‑speculative decoding framework for looped language models that reduces decoding latency by batching draft and verification steps within the same recurrent‑block calls. By exploiting intermediate recurrence outputs as draft predictions and weight sharing to process token states at different depths together, WFD arranges mixed‑depth states into a diagonal wavefront, allowing shallow‑depth drafting while deeper‑depth verification proceeds concurrently. Experiments on six Spec‑Bench task categories show WFD achieving up to 4.81× speedup on Huginn‑3.5B compared to autoregressive decoding, outperforming traditional draft‑then‑verify approaches.

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 Machine Learning.

Related stories

arXiv AI
Oct 16

LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models

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.

By Zixuan Liu, Nan Xue, Rui Bao, Yuhang Xu, Xiaofeng Cao, Jiangchao Yao, Zhiyong Chen
llms
More like this →
arXiv Computation and Language
Oct 7

DLoop: Looped Speculative Decoding

The paper introduces DLoop, a looped speculative decoding technique that adaptively performs multiple drafting stages before verification, allowing a draft model to continue generating tokens while confident. By verifying all accumulated draft tokens together and training the draft model to handle its own hidden states for unverified tokens, DLoop reduces the number of target‑model forward passes needed. Experiments across several speculative decoding methods show wall‑clock speedups of 5–41 % without sacrificing lossless decoding.

By Geonmo Gu, Byeongho Heo, HeeJae Jun, Yoohoon Kang, Sangmin Lee, Sangdoo Yun, Dongyoon Han
llms
More like this →
arXiv Machine Learning
Oct 28

One Call Ahead: Packed Self-Speculative Decoding with Diffusion Drafting

The paper introduces Packed-SS, a self‑speculative decoding schedule that combines drafting and verification into a single model call, reducing input size to linear in speculative width. By running the diffusion drafter one step ahead, the method keeps an unresolved suffix for the next iteration, avoiding the sequential wait of standard two‑call decoding. Experiments on Nemotron 3B/8B and SetDLM 1.7B demonstrate higher throughput and lower mean wall time under greedy decoding.

By Yuxiao Chen, Changxiao Cai
llmsdiffusionefficiency
More like this →