Fast-dLLM v2: Efficient Block-Diffusion LLM
Authors: name: Chengyue Wu, name: Hao Zhang, name: Shuchen Xue, name: Shizhe Diao, name: Yonggan Fu, name: Zhijian Liu, name: Pavlo Molchanov, name: Ping Luo, name: Song Han, name: Enze Xie
Organization: NVIDIA

Autoregressive (AR) large language models (LLMs) have achieved remarkable
performance across a wide range of natural language tasks, yet their inherent
sequential decoding limits inference efficiency. In this work, we propose
Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that
efficiently adapts pretrained AR models into dLLMs for parallel text
generation, requiring only approximately 1B tokens of fine-tuning. This
represents a 500x reduction in training data compared to full-attention
diffusion LLMs such as Dream (580B tokens), while preserving the original
model's performance. Our approach introduces a novel training recipe that
combines a block diffusion mechanism with a complementary attention mask,
enabling blockwise bidirectional context modeling without sacrificing AR
training objectives. To further accelerate decoding, we design a hierarchical
caching mechanism: a block-level cache that stores historical context
representations across blocks, and a sub-block cache that enables efficient
parallel generation within partially decoded blocks. Coupled with our parallel
decoding pipeline, Fast-dLLM v2 achieves up to 2.5x speedup over standard AR
decoding without compromising generation quality. Extensive experiments across
diverse benchmarks demonstrate that Fast-dLLM v2 matches or surpasses AR
baselines in accuracy, while delivering state-of-the-art efficiency among dLLMs
- marking a significant step toward the practical deployment of fast and
accurate LLMs. Code and model will be publicly released.