Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge

• URL: http://arxiv.org/abs/2606.08048v1
• Date: Sat, 06 Jun 2026 08:21:06 GMT
• Title: Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
• Abstract summary: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding.<n>The lack of token dependencies limits generation quality compared to autoregressive (AR) models.<n>Recent progress attempts to bridge the gap via importance sampling, with DLM being the proposal and AR being the target.<n>We introduce PoE-Bridge, a novel decoding framework that drastically improves generation speed and accuracy.
• Score: 93.37920675145553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressive (AR) models. Recent progress attempts to bridge the gap via importance sampling, with DLM being the proposal and AR being the target. However, due to the huge gap between their distributions, the sampling requires a large number of particles and is thus expensive to compute. In this paper, we introduce PoE-Bridge, a novel decoding framework that drastically improves generation speed and accuracy by introducing an intermediate distribution to bridge the gap. The distribution is constructed as a Product-of-Experts (PoE) of the DLM proposal and the AR target. With the intermediate distribution, we first use the DLM to draft multiple continuations in parallel, then apply rejection sampling to verify the drafted tokens and move the resulting candidates toward the PoE. We then use importance sampling to further correct the PoE-aligned candidates toward the AR target. We further propose several improved techniques, including mixed-temperature sampling for enhanced diversity and elastic rejection windows for reducing wasted verification. Empirically, PoE-Bridge achieves significantly improved accuracy with $5\times$ speedup over the standard DLM decoding approach, and recovers at least 95% of the target AR model's performance, efficiently advancing most of the quality gap on challenging mathematical reasoning and coding tasks. Our code is available at https://github.com/juntongshi48/poe-bridge.

Related papers

• Unlocking Lossless Speedups in LLMs via Discrete Diffusion [67.4336730000437]
  Large Language Models (LLMs) owe much of their success to next-token prediction (NTP)<n>We introduce diffusion-augmented LLMs, a new class of models that defines an AR model distribution.<n>We decouple the parameters of these models into two sets: AR weights, trained using the standard NTP objective, and lightweight diffusion weights, trained to generate multiple tokens simultaneously.
  arXiv  Detail & Related papers  (2026-09-03T15:48:43Z)
• ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping [79.1905186547363]
  ResiSpec is a framework that strategically reforms the proposal distribution during verification.<n>It prevents candidate obsolescence and achieves up to 1.92$times$ speedup over state-of-the-art multi-candidate methods.
  arXiv  Detail & Related papers  (2026-08-25T11:25:55Z)
• Introspective Diffusion Language Models [58.91876345013321]
  Introspective Diffusion Language Model (I-DLM) is a paradigm that retains parallel decoding while inheriting introspective consistency of AR training.<n>I-DLM uses a novel introspective strided decoding (ISD) algorithm, which enables the model to verify previously generated tokens while advancing new ones in the same forward pass.<n>I-DLM is the first DLM to match the quality of its same-scale AR counterpart while outperforming prior DLMs in both model quality and practical serving efficiency across 15 benchmarks.
  arXiv  Detail & Related papers  (2026-04-13T06:01:01Z)
• Entropy-Aware Speculative Decoding Toward Improved LLM Reasoning [3.6588919376939733]
  Speculative decoding (SD) accelerates large language model (LLM) reasoning by using a small draft model.<n>We propose Entropy-Aware Speculative Decoding (EASD), a training-free enhancement.
  arXiv  Detail & Related papers  (2025-12-29T00:45:19Z)
• Fast and Accurate Causal Parallel Decoding using Jacobi Forcing [41.89066334075016]
  Jacobi Forcing is a progressive distillation paradigm where models are trained on their own generated parallel decoding trajectories.<n>We introduce multi-block decoding with rejection recycling, which enables up to 4.5x higher token acceptance count per iteration and nearly 4.0x wall-clock speedup.
  arXiv  Detail & Related papers  (2025-12-16T18:45:18Z)
• Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model [98.35868970993232]
  Diffusion language models (DLMs) are emerging as a powerful and promising alternative to the dominant autoregressive paradigm.<n>We introduce efficient Sampling with Adaptive acceleration and Backtracking Enhanced Remasking (i.e., Saber) to achieve better inference speed and output quality in code generation.
  arXiv  Detail & Related papers  (2025-10-20T23:38:12Z)
• DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding [66.40658898418316]
  We present DiffuSpec, a training-free drop-in framework that uses a pretrained diffusion language model (DLM) to produce multi-token drafts in a single forward pass.<n>Across benchmarks, DiffuSpec yields up to 3x wall-clock speedup, establishing diffusion-based drafting as a robust alternative to autoregressive drafters for speculative decoding.
  arXiv  Detail & Related papers  (2025-09-28T07:00:15Z)
• Accelerating Diffusion LLMs via Adaptive Parallel Decoding [60.407727995313074]
  We introduce adaptive parallel decoding (APD), a novel method that dynamically adjusts the number of tokens sampled in parallel.<n>APD provides markedly higher throughput with minimal quality degradations on downstream benchmarks.
  arXiv  Detail & Related papers  (2025-05-31T06:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.