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新方法增强扩散语言模型的并行解码 · 追踪6个来源

作者 PulseAugur 编辑部 · [6 个来源] ·

研究人员正在探索改进扩散语言模型（DLM）并行解码的新方法。一种方法，可靠并行解码（RPD），侧重于根据层级预测稳定性和最终置信度来选择候选，在LLaDA和Dream等基准测试中实现了更高的吞吐量，同时保持了准确性。另一种方法PUMBA引入了一个统一的轨迹感知训练框架，该框架使训练和推理条件保持一致，从而提高了性能并减少了所需函数评估的次数。此外，对“软标记”的研究正在被重新审视，并提出了一种新的几何感知构造，以更好地解释这些标记如何在DLM中倾向于连贯的序列。最后，研究还在调查确定性扩散模型中的语义可访问性，并开发用于文本到图像模型的鲁棒性学习方法，以防止概念的再出现。 AI

影响 这些在扩散语言模型方面的进展可能导致更高效、更准确的文本生成，影响编码、数学和创意内容生成等领域的应用。

排序理由 arXiv上发表了多篇研究论文，详细介绍了扩散语言模型的新颖方法和分析。

在 arXiv cs.LG 阅读 →

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新方法增强扩散语言模型的并行解码 · 追踪6个来源

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arXiv上发表了多篇研究论文，详细介绍了扩散语言模型的新颖方法和分析。
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报道来源 [6]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenghao He, Bohan Liu, Guangzhi Xiong, Aidong Zhang ·

    Masked Diffusion Language Models 中的可靠并行解码

    arXiv:2609.36452v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same forward pass are not necessarily reliable when committed together. We study whe…

  2. arXiv cs.AI TIER_1 English(EN) · Manuel Madeira, Amitis Shidani, Alice Bizeul, Victor Turrisi, Louis B\'ethune, Bhavika Devnani, Dan Busbridge, Pierre Ablin, Jo\~ao Monteiro ·

    关于掩码扩散语言模型的轨迹感知训练

    arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The model is trained on randomly masked sequences, whereas inference follows a traject…

  3. arXiv cs.LG TIER_1 English(EN) · Kodai Kawamura, Kenji Kawaguchi, Anji Liu ·

    重新思考扩散语言模型中并行解码的软Token

    arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate individually plausible but mutually inconsistent tokens. Recent work shows that \…

  4. arXiv cs.LG TIER_1 English(EN) · Nikita Gushchin, Dmitry Baranchuk, Alexander Korotin ·

    Alpha Diffusion 语言模型：仅靠因子分解并非问题所在

    arXiv:2609.38066v1 Announce Type: new Abstract: Discrete diffusion language models can generate multiple tokens in parallel, but reducing the number of denoising steps can lead to inconsistent predictions. Standard cross-entropy training fits conditional token marginals, whereas …

  5. arXiv cs.LG TIER_1 English(EN) · Kuntian Chen, Wei Wei, Yizhou Zeng, Sophie Langer, Mariia Seleznova, Hung-Hsu Chou ·

    从种子到语义：测量确定性扩散模型中的语义可访问性

    arXiv:2602.06155v2 Announce Type: replace Abstract: Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along this sampling process. We study this question in deterministic samplers by measuri…

  6. arXiv cs.CV TIER_1 English(EN) · Arian Komaei Koma, Seyed Amir Kasaei, Aida Aryafar, Matin Ghiasi, Ali Aghayari, Amirhossein Souri, Mohammad Mosayyebi, AmirMahdi Sadeghzadeh, Mohammad Hossein Rohban ·

    剔除劣质种子：文本到图像扩散模型的初始噪声鲁棒性遗忘

    arXiv:2609.37537v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive retraining. However, we reveal that current state-of-the-art (SOTA) approaches are …

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