新的R2D推理技术提升了掩码扩散模型在逻辑任务上的表现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为随机重掩码-去噪（R$^2$D）的新型推理技术，用于掩码扩散模型（MDMs）。该方法将MDM推理重新构建为马尔可夫链，并引入重掩码-去噪核来分析生成轨迹。R$^2$D是一种测试时、无需微调的程序，可应用于现有模型。此外，还提出了轨迹校正训练（TCT）方法，通过让模型暴露于自身错误来改进去噪器训练。该方法在逻辑任务上显示出显著的性能提升，在数独和3-SAT上实现了高精度，并且与现有的迭代校正方法和LLaDA2.1-mini相比，在语言任务上的性能也有所提高。 AI

影响引入了一种新颖的推理方法，显著提高了掩码扩散模型在逻辑和语言任务上的性能。

排序理由详细介绍掩码扩散模型新推理技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhaoyi Zhou, Frank Wu, Andrea Zanette · 2026-10-23 04:00

Random Remasking Scales Masked Diffusion Inference

arXiv:2610.30744v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking tokens, but errors introduced during decoding are typically irreversible. Recent work has shown that iterative correction can improve sample quality, yet e…

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Random Remasking Scales Masked Diffusion Inference

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