LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding
Diffusion Large Language Models (dLLMs) have demonstrated significant potential for high-speed inference. However, current confidence-driven decoding strategies are constrained by limited parallelism, typically achieving only 1--3 tokens per forward pass (TPF). In this work, we identify that the degree of parallelism during dLLM inference is highly sensitive to the Token Filling Order (TFO). Then, we introduce Lookahead PArallel Decoding LoPA, a training-free, plug-and-play algorithm, to identify a superior TFO and hence accelerate inference. LoPA concurrently explores distinct candidate TFOs via parallel branches, and selects the one with the highest potential for future parallelism based on branch confidence. We apply LoPA to the state-of-the-art D2F model and observe a substantial enhancement in decoding efficiency. Notably, LoPA increases the TPF of D2F-Dream to 10.1 on the GSM8K while maintaining performance superior to the Dream baseline. Furthermore, to facilitate this unprecedented degree of parallelism, we develop a specialized multi-device inference system featuring Branch Parallelism (BP), which achieves a single-sample throughput of 1073.9 tokens per second under multi-GPU deployment. The code is available at https://github.com/zhijie-group/LoPA.
694a70d6335742716e932442
Chenkai Xu
694a70d6335742716e932443
Yijie Jin
claimed_verified
2025-12-25T20:47:24.582Z
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/avatars/f5be15e754f0fbbb37d2cc5ea417f729.svg
Yijie Jin
DrewJin0827
user
DrewJin0827
694a70d6335742716e932444
Jiajun Li
694a70d6335742716e932445
Yi Tu
694a70d6335742716e932446
Guoping Long
694a70d6335742716e932447
Dandan Tu
694a70d6335742716e932448
Mingcong Song
694a70d6335742716e932449
Hongjie Si
694a70d6335742716e93244a
Tianqi Hou
694a70d6335742716e93244b
Junchi Yan
694a70d6335742716e93244c
Zhijie Deng
LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding
Diffusion Large Language Models (dLLMs) have demonstrated significant potential for high-speed inference. However, current confidence-driven decoding strategies are constrained by limited parallelism, typically achieving only 1--3 tokens per forward pass (TPF). In this work, we identify that the degree of parallelism during dLLM inference is highly sensitive to the Token Filling Order (TFO). Then, we introduce Lookahead PArallel Decoding LoPA, a training-free, plug-and-play algorithm, to identify a superior TFO and hence accelerate inference. LoPA concurrently explores distinct candidate TFOs via parallel branches, and selects the one with the highest potential for future parallelism based on branch confidence. We apply LoPA to the state-of-the-art D2F model and observe a substantial enhancement in decoding efficiency. Notably, LoPA increases the TPF of D2F-Dream to 10.1 on the GSM8K while maintaining performance superior to the Dream baseline. Furthermore, to facilitate this unprecedented degree of parallelism, we develop a specialized multi-device inference system featuring Branch Parallelism (BP), which achieves a single-sample throughput of 1073.9 tokens per second under multi-GPU deployment. The code is available at https://github.com/zhijie-group/LoPA.
LoPA, a training-free algorithm, enhances the parallelism of diffusion large language models, doubling the tokens per forward pass and boosting throughput with multi-GPU deployment.
Diffusion Large Language Models dLLMs confidence-driven decoding Token Filling Order TFO Lookahead PArallel Decoding LoPA branch confidence D2F model GSM8K Dream baseline Branch Parallelism BP multi-device inference system single-sample throughput