Speculative Diffusion Paper Maps the Speed-Quality Pareto Frontier via Two Tradeoffs
UCLA · hf · 2026-10-26
The paper explores trading inference speed for quality when diffusion LLMs serve as speculative decoding draft models. Unlike lossless autoregressive speculative decoding, dLLMs speed up by degrading quality via less diffusion effort. The authors generalize speculative decoding to be lossy through lenient acceptance criteria, and show that jointly tuning two parameters — diffusion effort and acceptance leniency — extends the speed-quality Pareto frontier.
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