# Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles

arXiv: https://arxiv.org/abs/2506.10848 · HTML: https://mlsanity.com/paper/2506.10848/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Simplified and Generalized Masked Diffusion for Discrete Data](https://mlsanity.com/paper/2406.04329/) (2024)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [A Continuous Time Framework for Discrete Denoising Models](https://mlsanity.com/paper/2205.14987/) (2022)
- [Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding](https://mlsanity.com/paper/2505.22618/) (2025)
- [Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models](https://mlsanity.com/paper/2503.09573/) (2025)
- [The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"](https://mlsanity.com/paper/2309.12288/) (2023)
- [Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling](https://mlsanity.com/paper/2409.02908/) (2024)
- [Scaling up Masked Diffusion Models on Text](https://mlsanity.com/paper/2410.18514/) (2024)
- [dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching](https://mlsanity.com/paper/2506.06295/) (2025)
- [dKV-Cache: The Cache for Diffusion Language Models](https://mlsanity.com/paper/2505.15781/) (2025)

## Built on by (most cited first, 30 of 45)

- [Fast-dLLM v2: Efficient Block-Diffusion LLM](https://mlsanity.com/paper/2509.26328/) (2025)
- [Diffusion Language Models Know the Answer Before Decoding](https://mlsanity.com/paper/2508.19982/) (2025)
- [DPad: Efficient Diffusion Language Models with Suffix Dropout](https://mlsanity.com/paper/2508.14148/) (2025)
- [A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models](https://mlsanity.com/paper/2508.08712/) (2025)
- [Learning to Parallel: Accelerating Diffusion Large Language Models via Learnable Parallel Decoding](https://mlsanity.com/paper/2509.25188/) (2025)
- [Confidence-Based Decoding is Provably Efficient for Diffusion Language Models](https://mlsanity.com/paper/2603.22248/) (2026)
- [Soft-Masked Diffusion Language Models](https://mlsanity.com/paper/2510.17206/) (2025)
- [Thinking Inside the Mask: In-Place Prompting in Diffusion LLMs](https://mlsanity.com/paper/2508.10736/) (2025)
- [CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit](https://mlsanity.com/paper/2510.06133/) (2025)
- [Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules](https://mlsanity.com/paper/2512.02892/) (2025)
- [Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion](https://mlsanity.com/paper/2510.04525/) (2025)
- [dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning](https://mlsanity.com/paper/2512.21446/) (2025)
- [Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Models](https://mlsanity.com/paper/2604.00375/) (2026)
- [Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference](https://mlsanity.com/paper/2602.22868/) (2026)
- [Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models](https://mlsanity.com/paper/2604.08964/) (2026)
- [Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models](https://mlsanity.com/paper/2604.10567/) (2026)
- [Locally Coherent Parallel Decoding in Diffusion Language Models](https://mlsanity.com/paper/2603.20216/) (2026)
- [Just on Time: Token-Level Early Stopping for Diffusion Language Models](https://mlsanity.com/paper/2602.11133/) (2026)
- [Drifting Objectives for Refining Discrete Diffusion Language Models](https://mlsanity.com/paper/2605.19470/) (2026)
- [FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration](https://mlsanity.com/paper/2605.20022/) (2026)
- [Sangam: Efficiently Serving Diffusion LLMs with the AR Stack](https://mlsanity.com/paper/2607.04206/) (2026)
- [C2DLM: Causal Concept-Guided Diffusion Large Language Models](https://mlsanity.com/paper/2511.22146/) (2025)
- [STDec: Spatio-Temporal Stability Guided Decoding for dLLMs](https://mlsanity.com/paper/2604.06330/) (2026)
- [Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation](https://mlsanity.com/paper/2605.30753/) (2026)
- [Supportive Token Revealing for Fast Diffusion Language Model Decoding](https://mlsanity.com/paper/2606.04236/) (2026)
- [VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination](https://mlsanity.com/paper/2606.17999/) (2026)
- [Understanding Parallel Samplers in Masked Diffusion via Random Walks on Graphs](https://mlsanity.com/paper/2606.22976/) (2026)
- [Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs](https://mlsanity.com/paper/2607.14107/) (2026)
- [Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models](https://mlsanity.com/paper/2608.11742/) (2026)
- [STaRR: Spatial-Temporal Token-Dynamics-Aware Responsive Remasking for Diffusion Language Models](https://mlsanity.com/paper/2601.04205/) (2025)

## Abstract (arXiv)

Diffusion-based language models (dLLMs) have emerged as a promising alternative to traditional autoregressive LLMs by enabling parallel token generation and significantly reducing inference latency. However, existing sampling strategies for dLLMs, such as confidence-based or semi-autoregressive decoding, often suffer from static behavior, leading to suboptimal efficiency and limited flexibility. In this paper, we propose SlowFast Sampling, a novel dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. Our method is guided by three golden principles: certainty principle, convergence principle, and positional principle, which govern when and where tokens can be confidently and efficiently decoded. We further integrate our strategy with dLLM-Cache to reduce redundant computation. Extensive experiments across benchmarks and models show that SlowFast Sampling achieves up to 15.63× speedup on LLaDA with minimal accuracy drop, and up to 34.22× when combined with caching. Notably, our approach outperforms strong autoregressive baselines like LLaMA3 8B in throughput, demonstrating that well-designed sampling can unlock the full potential of dLLMs for fast and high-quality generation.