parallel-decoding

FastGuide 是一种自适应的并行与自回归混合解码方法，通过复用奖励模型反向传播计算、利用 KV 缓存和稀疏注意力重计算来加速扩散大语言模型的梯度奖励引导，在三个奖励基准上实现最高 4.4× 加速同时保持相近生成质量。

This paper proposes LUDI, a less uniform diffusion language modeling framework that fixes over-uniform training objectives and condition-target confusion in uniform diffusion LMs, enabling a 7B-scale UDLM with 3x-token-per-step speedup over autoregressive decoding and competitive complex reasoning performance.

WaveFront Decoding introduces a training-free self-speculative decoding framework for looped language models that reduces latency by concurrently batching drafting and verification, achieving up to 4.81x speedup on Huginn-3.5B.

Flash-dLLM is a training-free inference acceleration framework for diffusion LLMs that uses IO-aware KV caching and parallel decoding to achieve significant speedups and memory efficiency improvements.

The paper introduces a two-stage LLM pipeline using fine-tuned Qwen3-4B with Hyper-Parallel Decoding to extract purchase-discriminative attributes in e-commerce, achieving 85% accuracy with 92% cost reduction.

Alibaba PAI releases LoRA checkpoints for accelerating MiniMax-H3 video generation using Parallel Decoding Distillation, enabling efficient inference in 8 steps.

The paper introduces Consistency Forcing (CForce), a distillation technique for diffusion large language models that improves parallel decoding by aligning early-stage predictions with later stages, enhancing speed-quality trade-offs.

Proposes Ripple-Pivot Search, a training-free decoding method for diffusion large language models that proactively commits mid-entropy pivot positions to reduce uncertainty and accelerate parallel decoding, achieving 4-10x speedup.

PaDoc introduces a layout-grounded parallel decoding method for end-to-end document parsers, decoupling layout and content decoding to reduce decoding depth and improve throughput. It achieves state-of-the-art results on OmniDocBenchFull and significantly speeds up inference compared to sequential baselines.

NVIDIA introduces Parallel Decoding Distillation (PDD) for accelerating image and video generation, enabling high-quality outputs with fewer neural function evaluations on models like LTX-2.3 and Wan2.1-14B.

Parallel Decoding Distillation (PDD) is a trajectory-based distillation method that accelerates image and video generation by predicting multiple denoising steps per network evaluation, achieving state-of-the-art performance with 4-8 NFEs on models like LTX-2.3, Wan14B, and Qwen-Image.

Proposes DC-Leap, a training-free framework that accelerates diffusion large language models by introducing dynamic contiguous verification and draft-guided decoding, achieving up to 105× speedup with comparable generation quality.

HPD-Parsing introduces a hierarchical parallel decoding paradigm for VLM-based document parsing, replacing full-page autoregressive generation to achieve 4,752 tokens per second throughput (2.62x faster than prior models) while maintaining competitive accuracy.

Proposes AdaLook, an adaptive multi-step lookahead decoding framework for masked diffusion language models that dynamically determines rollout depth and branch expansion based on candidate-score variance, achieving better accuracy-decoding steps trade-off compared to existing one-step lookahead decoding methods.

Spatially Speculative Decoding (SSD) accelerates autoregressive image models by predicting entire rows in parallel using small helper networks, achieving up to 13.28x speedup while maintaining benchmark performance.

DeLS-Spec decouples long- and short-context modeling in speculative decoding by adding a lightweight local head to DFlash, achieving consistent speedups without full retraining. It requires only standard next-token prediction training for the local head and improves acceptance length on Qwen3 benchmarks.

This paper introduces PadCaptioner, a 3B parameter model for omni-modal dense video captioning that uses parallelized autoregressive decoding to achieve high efficiency and quality, outperforming 7B counterparts. A latent planning mechanism enables lossless parallel generation by exploiting weak local dependencies among events.

This paper identifies evaluation inconsistencies in diffusion LLM decoding methods, showing that prompt template choice significantly impacts rankings, and proposes guidelines for reliable evaluation.

This paper proposes Dynamic-dLLM, a training-free framework that accelerates diffusion large language models by dynamically allocating cache-update budgets and calibrating decoding thresholds, achieving over 3x speedup on models like LLaDA and Dream while maintaining performance.

Speculative decoding is an inference optimization technique that uses a fast draft model to propose future tokens verified in parallel by a larger model, improving LLM generation speed. The article highlights its trending status on Papers with Code and a recent SGLang blog post about state-of-the-art latencies using DFlash models.