深度揭秘DeepSeek最新研究：投机解码如何大幅加速LLM推理，论文全文在此。
Article URL: https://github.com/deepseek-ai/DeepSpec/blob/main/DSpark_paper.pdf Comments URL: https://news.ycombinator.com/item?id=48696585 Points: 52…
投机解码新突破：提出LK损失函数直接优化接受率，加速大模型推理生成
arXiv:2602.23881v2 Announce Type: replace Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a li…
自适应投机解码新方法AdaSD，大幅提升大模型推理效率，值得关注的最新论文成果
arXiv:2512.11280v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, bu…
针对大模型推理中投机解码的验证瓶颈，提出层次化投机解码方法，显著提升推理速度。
arXiv:2510.01336v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by using a smaller draft model to speculate t…
投机解码将小模型驻留DRAM，突破手机LLM推理的内存瓶颈，实现高效闪存大模型推理
arXiv:2605.16786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly needed for interactive mobile applications, but high-qua…
强化学习驱动自适应投机训练，统一训练与推理流程，消除部署延迟，加速大模型服务。
arXiv:2602.06932v4 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentan…