研究人员引入了双焦扩散语言模型（dLLMs），以解决离散扩散模型中生成质量和推理速度之间的权衡问题。新的范例，以 R2LM（从右到左的 Mamba）为例，使用非对称双向上下文来实现高质量和高效的 KV 缓存。实验表明，R2LM 在吞吐量方面显著优于双向 dLLMs 和自回归基线，同时保持了具有竞争力的生成质量。 AI
影响 引入了一种新颖的架构，可在不牺牲生成质量的情况下显著提高扩散语言模型的推理速度。
排序理由 该集群包含一篇详细介绍新模型架构和实验结果的学术论文。
AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →
arXiv:2606.29215v1 Announce Type: cross Abstract: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. A natural next step is to extend them from Single-Block Diffusion (SingleBD) to Multi-Block Diffusion…
arXiv:2606.29094v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to conventional autoregressive language models. By generating multiple tokens in parallel during each denoising step, they offer higher inference thro…
arXiv:2606.29275v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets. This creates a mismatch between training and inference, where models must operate over arbi…
arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. However, such promising frameworks face a fundamental architectural design dilemma: \…
Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. However, such promising frameworks face a fundamental architectural design dilemma: \ding{182} Adopting bidirectional attention achieve…