SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding
Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional context. Despite their ability to update multiple positions in parallel, inference remains costly due to the many denoising steps required for high-quality generation. We propose SAID, a Scaffold-Aware Iterative Decoding framework that accelerates DLLMs by reallocating computation across tokens. SAID first spends denoising computation on scaffold tokens to establish the coarse semantic structure, and then completes predictable detail tokens with fewer steps. We further adapt SAID to block-wise diffusion decoding and introduce Confidence-Hierarc...
| Authors | Na Li, Chengda Wang, Mingju Gao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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