D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models

• URL: http://arxiv.org/abs/2606.04446v1
• Date: Wed, 03 Jun 2026 04:48:00 GMT
• Title: D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models
• Abstract summary: Recent diffusion-based drafters generate an entire block of tokens in parallel but usually commit to a single draft sequence per verification.<n>We propose D2SD, a dual diffusion draft speculative decoding framework that organizes candidates into a confidence-guided prefix tree.<n>The resulting shared-guided candidates are jointly verified via cascade attention.
• Score: 27.44373450962651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass. Recent diffusion-based drafters generate an entire block of tokens in parallel but usually commit to a single draft sequence per verification: once the first mismatch occurs, all subsequent draft tokens are discarded, resulting in a limited acceptance rate. Naively batching more draft candidate sequences only introduces a marginal improvement, as redundant or poorly placed branches increase the cost of drafting and verification without proportionally increasing the number of accepted tokens. We propose D^2SD, a dual diffusion draft speculative decoding framework that organizes candidates into a confidence-guided prefix tree, where the first diffusion drafter generates a block along with per-position confidence scores that are used to identify the most likely rejection boundary and select the top-K prefix ranges for recovery; the second variable-prefix diffusion drafter re-anchors at each selected prefix and proposes alternative continuations in one batched pass; the resulting shared-prefix candidates are jointly verified via cascade attention. Empirically, D^2SD shows clear improvements over both the underlying diffusion approach and strong autoregressive speculative decoding baselines.

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