Trajectory-Level Speculative Decoding for Diffusion Language Models
Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to single-token generation under low confidence, severely limiting throughput. Unlike autoregressive models where speculative decoding operates on token sequences in a fixed left-to-right order, dLLMs require speculating over denoising trajectories-sequences of multi-token updates with explicit positions and unmasking orders. We develop a trajectory-level speculative framework that constructs draft denoising trajectories via confidence-stratified tree exploration and verifies them through blockwise parallel evaluation with bidirectional attention ...
| Authors | Tianxiang Pan, Baitao Gong, Mo Guang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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