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Diffusion language models research tackles efficiency and confidence gaps · 6 sources tracked

作者 PulseAugur 编辑部 · [8 个来源] ·

近期研究探索了提高扩散语言模型（DLM）效率和有效性的方法。一篇论文研究了在解码过程中何时真正需要无分类器引导（CFG），认为其益处是特定于提示的，并且通常集中在过程的早期。另一项研究介绍了 Archer，一种无需训练的 KV 缓存方法，通过自适应地重用提示隐藏状态来加速 DLM 中的回滚能力。进一步的研究解决了 DLM 中的“表示-置信度差距”问题，即内部准确性信号与外部置信度分数不一致，并提出了一个轻量级工具来改进答案排名。此外，还提出了一种名为 PCD 的新预训练目标，通过将训练接口与提示条件生成对齐，以减少 DLM 中预训练和生成之间的不匹配。最后，介绍了一种名为粒子 Gibbs 采样（PG-DLM）的推理时轨迹细化方法，允许在不重新训练的情况下将 DLM 引导至期望的奖励，并通过细化迭代实现扩展。 AI

影响 这些进展旨在提高扩散语言模型的效率、可靠性和可控性，有望在各种生成任务中带来更好的性能。

排序理由 多篇关于扩散语言模型的 arXiv 论文发表，详细介绍了新方法和分析。

在 arXiv cs.LG 阅读 →

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Diffusion language models research tackles efficiency and confidence gaps · 6 sources tracked

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多篇关于扩散语言模型的 arXiv 论文发表，详细介绍了新方法和分析。
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报道来源 [8]

  1. arXiv cs.AI TIER_1 English(EN) · Yifan Wu, Yufeng Zhang, Kenli Li ·

    CORA-Diff：面向高效扩散语言模型推理的置信度导向残差接受

    arXiv:2608.11235v1 Announce Type: new Abstract: Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize early, but blockwise decoding continues until all positions are resolved, causin…

  2. arXiv cs.LG TIER_1 English(EN) · Theo X. Olausson, Metod Jazbec, Xi Wang, Armando Solar-Lezama, Christian A. Naesseth, Stephan Mandt, Eric Nalisnick ·

    两个温度的故事：扩散语言模型的简单、高效和多样化采样

    arXiv:2604.09921v2 Announce Type: replace Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on the tradeoff between speed and quality of individual samples; how to additiona…

  3. arXiv cs.CL TIER_1 English(EN) · Xuning He, Zinan Sheng, Yongding Tao, Huanyu Liu, Ge Li, Xue Jiang, Yihong Dong ·

    Archer：用于扩散语言模型中高效回滚的缓存隐藏状态的自适应重用

    arXiv:2608.08086v1 Announce Type: new Abstract: Diffusion language models (DLMs) iteratively refine a sequence, allowing earlier predictions to be revised as context evolves. This rollback capability distinguishes them from irreversible autoregressive generation, but makes infere…

  4. arXiv cs.CL TIER_1 English(EN) · Xiaocheng Lu, Huabin Liu, Song Guo, Jianguo Li ·

    减少扩散语言模型中的预训练-生成不匹配

    arXiv:2608.09424v1 Announce Type: new Abstract: Autoregressive language models align training and use: generation conditions on a clean prompt, and training predicts future tokens from clean left context. Diffusion language models offer parallel denoising, but native dLLM pretrai…

  5. arXiv cs.CL TIER_1 English(EN) · Saurabh Yadav, Badri Narayana Patro, Vijay Srinivas Agneeswaran ·

    不确定但确定：揭示扩散语言模型中的表征-置信度差距

    arXiv:2608.08791v1 Announce Type: new Abstract: Diffusion language models use broad context to create text, suggesting they might handle input noise better than standard models. Testing reveals this is only partially true. Internally, diffusion models detect text errors highly ac…

  6. arXiv cs.CL TIER_1 English(EN) · Fan Zhou, Weitian Wang, Tim Van de Cruys ·

    承诺先于实现：当无分类器引导在掩码扩散语言模型中变得不必要时

    arXiv:2608.08082v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is usually kept on throughout masked diffusion language model decoding, although its benefit varies across prompts and over time. We study when CFG is actually needed by comparing, from any partial out…

  7. arXiv cs.CL TIER_1 English(EN) · Lavanya Nigam, Ishaan Bansal, Aryan Sood, Vidit Aggarwal, Gaurav Kumar Nayak ·

    插值中的迷失：为何预测性反馈在扩散语言模型中会失效

    arXiv:2608.06529v1 Announce Type: new Abstract: Soft-masking accelerates the convergence of Masked Diffusion Language Models (MDLMs). Existing formulations build this blend with linear interpolation (LERP) in the raw embedding space, which implicitly treats that space as Euclidea…

  8. arXiv cs.LG TIER_1 English(EN) · Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon ·

    通过轨迹精炼实现扩散语言模型的推理时缩放

    arXiv:2507.08390v5 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training. However, inference-time control remains relatively underexplored.…

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