The paper introduces "draft trees" to accelerate diffusion model sampling by allowing non‑linear lookahead drafts, thereby increasing acceptance rates per expensive target evaluation. It connects speculative sampling to relative entropy coding, adopts greedy rejection sampling as the draft‑target coupling, and demonstrates up to 8.3% speed‑up over reflection coupling baselines in experiments.
Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is difficult because speculative sampling requires drawing from a residual distribution.
arXiv:2606. 00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.
arXiv:2607. 22634v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, generating tokens in parallel.
The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.
arXiv:2609.06761v1 Announce Type: new Abstract: While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharac...
arXiv:2606. 04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass.
arXiv:2509. 18085v4 Announce Type: replace-cross Abstract: Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token-generation rates.
arXiv:2602. 05774v4 Announce Type: replace-cross Abstract: Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths.
The efficiency of Large Language Model (LLM) serving is fundamentally limited by the sequential nature of autoregressive decoding. Speculative Decoding (SD) mitigates this by using a lightweight draft...
ResiSpec is a framework that improves speculative decoding for large language models by reshaping the residual distribution during verification. It addresses the problem of residual drift, where rejected candidates cause the target distribution to diverge from the draft model’s predictions, rendering later candidates ineffective. By aligning the verification process with the draft model’s high‑confidence regions, ResiSpec prevents candidate obsolescence and achieves up to 1.92× speedup over existing multi‑candidate methods.
arXiv:2604. 09921v2 Announce Type: replace Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs).
arXiv:2602. 03211v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent.