arXiv:2610.00623v1 Announce Type: new
Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where li...
By Wenhan Yang, Anirudh Rao, Ashwin Chandra
arXiv:2609.36590v1 Announce Type: cross
Abstract: Self-speculative decoding accelerates large language model (LLM) inference by drafting tokens from the target model itself, but faces a sharp tradeof...
By Hankun Lin, Patrick Pynadath, Ruqi Zhang
arXiv:2608. 10362v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to speculate multiple tokens, reducing expensive target model decoding steps.
By Eunjeong Kim, Yeong Jun Jeon, Myeonggyun Han
arXiv:2602. 20217v2 Announce Type: replace-cross Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore the dynamic computational overhead of attention in long-context scenarios.
By Seongjin Cha, Gyuwan Kim, Dongsu Han, Tao Yang, Insu Han
arXiv:2610.01687v1 Announce Type: cross
Abstract: Test-time scaling often seeks better answers by sampling multiple responses from a frozen model, yet conventional temperature sampling generates ever...
By Akshit Singh, Shyam Marjit, Wei Lin, Leonid Karlinsky, M. Jehanzeb Mirza
arXiv:2411. 05894v3 Announce Type: replace-cross Abstract: Speculative Decoding has emerged as a popular technique for accelerating inference in Large Language Models.
By Michele Marzollo, Jiawei Zhuang, Niklas Roemer, Niklas Zwingenberger, Lorenz K. M\"uller, Lukas Cavigelli