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Parallel Sampling via Autospeculation

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
On November 12, 2025, researchers unveiled parallel algorithms designed to accelerate sampling in autoregressive and denoising diffusion models, a significant advancement in the field of artificial intelligence. Traditional sequential sampling methods required O(n) time, but the new approach, leveraging speculative rejection sampling, reduces the expected sampling time to O(n^{1/2}). This improvement surpasses the previous O(n^{2/3}) bound for autoregressive models and represents the first parallel speedup for diffusion models in high-accuracy scenarios. The technique is inspired by speculative decoding methods used in large language models, highlighting its innovative nature. The results hinge on the assumption that the support of the target distribution is bounded, making this advancement particularly relevant for applications that demand high efficiency and accuracy in sampling.
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