decoding-efficiency

PreSTO 提出一种预测性子树预取方法加速 PowerMH 功率采样器，通过批量预取未来的 MH 提案并保持转移核不变，在七个数据集和五个 LLM 上实现 1.71×–2.33× 的中位加速。

NVIDIA released the Nemotron-Labs-Diffusion model family (3B to 14B) that supports both AR and diffusion decoding with novel self-speculation, achieving significant speedups (up to 4x) over standard AR and Eagle3 methods across hardware platforms.

R²-dLLM introduces spatio-temporal redundancy reduction techniques that cut diffusion LLM decoding steps by up to 75% while preserving generation quality, addressing a key deployment bottleneck.