On the SPOT: Enhancing Speculative Decoding for Diffusion Language Models via Commit-Order Prediction

Diffusion language models (DLMs) promise parallel text generation, but in prac- tice face an unresolved speed–quality trade-off. Exact decoding fills in only one masked position per model pass, while faster multi-token decoding schemes are lossy. Speculative decoding is the standard lossless acceleration for autoregres- sive models. However, it does not transfer directly to DLMs, which must choose both what to generate and where. We show that this order is the missing piece: the model’s own predictions from the previous pass already give the right token at 92–100% of positions, but not which position comes next. Based on this ob- servation, we introduce Speculating Positions Order Trajectory (SPOT), which learns to predict this order. A 0.62M-parameter model reads only the target’s out- put distributions and proposes a tree of candidate continuations, and the target verifies all of them in a single pass by checking whether each equals its own next state. On the verification side, our analysis shows that prior block-diffusion verifi- cation approaches either assume T =0 or a sequential proposal interface. To sup- port tree-based speculation at any temperature, we propose using coupled-noise match verification for diffusion blocks, following shared-Gumbel-noise coupling approaches. Our comprehensive experiments show that SPOT cuts target passes by up to 6.8× and reaches up to 3.2× the throughput of standard decoding, beating the calibrated and training-free drafting baselines in every task and budget at T =0 and T =1. In addition, we show that the learned drafter transfers across models, demonstrating successful transfer across six different DLMs. Code is available at: https://anonymous.4open.science/r/spot-speculative-decoding-E354