Unverified50% confidenceFactExact time
Diffusion draft models can denoise multiple draft tokens in parallel in a single forward pass, making them an important approach for accelerating speculative decoding.
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50%
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Long-term
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11/5/2026
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UnverifiedDiffusion language models (such as LLaDA and Dream) use iterative masked decoding, starting from a fully masked sequence and progressively revealing tokens over several denoising steps, with each step applying bidirectional attention over the entire sequence, so the representations of already revealed tokens are updated by newly revealed tokens.77% similarVerifiedDiffusion language models adopt a parallel denoising approach, which can simultaneously optimize outputs at multiple positions in a single forward pass and then iteratively converge to the final text through multiple rounds.76% similarUnverifiedTraditional speculative decoding relies on an independent, smaller draft model to serially generate candidate tokens, which are then verified in parallel by the large model.75% similarUnverifiedSpeculative decoding technology has a lightweight draft model first generate candidate token sequences, which are then verified in parallel by the main model, delivering a 3–5x speed improvement compared to sequential token-by-token generation.75% similar
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