Update README.md

README.md

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 Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks. However, their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that transforms a pretrained AR model—specifically, Qwen-2.5-1.5B-Instruct—into a diffusion-style decoder for parallel text generation.
 
-Our approach introduces a novel decoding recipe incorporating a complementary attention mask and a position-aware masking strategy, which together enable blockwise bidirectional context modeling while preserving the original AR training objectives and performance. To further enhance inference speed, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations and a token-level intra-block cache that supports efficient parallel decoding within partially generated blocks.
+Our approach introduces a novel decoding recipe incorporating a complementary attention mask and block diffusion mechanism, which together enable blockwise bidirectional context modeling while preserving the original AR training objectives and performance. To further enhance inference speed, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations and a sub-block level cache that supports efficient parallel decoding within partially generated blocks.
 
 Coupled with our parallel decoding pipeline, Fast-dLLM v2 achieves a near 2.5x speedup over standard AR decoding, without compromising generation quality. Extensive experiments demonstrate that Fast-dLLM v2 achieves state-of-the-art trade-offs between efficiency and performance among existing diffusion-based LLMs, marking a significant step toward practical deployment of fast and accurate language models.