研究人员推出了一种名为 K-Forcing 的新范式，通过同时解码多个 token 来加速语言模型推理。这种推前方法将现有的自回归模型提炼成一个映射，该映射可以在一次通过中生成 k 个 token。K-Forcing 旨在提高高负载批量服务场景的效率，这是大规模 LLM 部署的关键领域。初步评估显示，在质量影响适中的情况下，速度提高了 2.4-3.5 倍。 AI
影响 为 LLM 在高负载部署场景下的自回归生成提供了一条有前景的加速途径。
排序理由 该集群包含一篇详细介绍语言模型推理新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2606.10820v1 Announce Type: cross Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient. Existing acceleration approaches, such as speculative dec…
Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient. Existing acceleration approaches, such as speculative decoding and diffusion language models, can yield spe…
Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient. Existing acceleration approaches, such as speculative decoding and diffusion language models, can yield spe…