# DPad: Efficient Diffusion Language Models with Suffix Dropout

arXiv: https://arxiv.org/abs/2508.14148 · HTML: https://mlsanity.com/paper/2508.14148/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Placed in Discrete and text diffusion, one of 14 directions in Diffusion models.
- Builds on 28 of its 32 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- Diffusion models / [Discrete and text diffusion](https://mlsanity.com/diffusion/discrete-and-text-diffusion/)

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution](https://mlsanity.com/paper/2310.16834/) (2023)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding](https://mlsanity.com/paper/2505.22618/) (2025)
- [The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"](https://mlsanity.com/paper/2309.12288/) (2023)
- [SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning](https://mlsanity.com/paper/2012.09852/) (2020)
- [Scaling up Masked Diffusion Models on Text](https://mlsanity.com/paper/2410.18514/) (2024)
- [DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation](https://mlsanity.com/paper/2506.20639/) (2025)
- [Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference](https://mlsanity.com/paper/2508.02193/) (2025)
- [dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching](https://mlsanity.com/paper/2506.06295/) (2025)
- [Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction](https://mlsanity.com/paper/2508.02558/) (2025)
- [Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles](https://mlsanity.com/paper/2506.10848/) (2025)

## Built on by (most cited first, 30 of 39)

- [LLaDA2.0: Scaling Up Diffusion Language Models to 100B](https://mlsanity.com/paper/2512.15745/) (2025)
- [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)
- [dParallel: Learnable Parallel Decoding for dLLMs](https://mlsanity.com/paper/2509.26488/) (2025)
- [DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding](https://mlsanity.com/paper/2510.02358/) (2025)
- [AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size](https://mlsanity.com/paper/2509.26432/) (2025)
- [DMax: Aggressive Parallel Decoding for dLLMs](https://mlsanity.com/paper/2604.08302/) (2026)
- [FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models](https://mlsanity.com/paper/2509.20624/) (2025)
- [CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit](https://mlsanity.com/paper/2510.06133/) (2025)
- [How Efficient Are Diffusion Language Models? A Critical Examination of Efficiency Evaluation Practices](https://mlsanity.com/paper/2510.18480/) (2025)
- [Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models](https://mlsanity.com/paper/2512.02044/) (2025)
- [TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration](https://mlsanity.com/paper/2602.08404/) (2026)
- [Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding](https://mlsanity.com/paper/2601.17917/) (2026)
- [ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping](https://mlsanity.com/paper/2603.10088/) (2026)
- [Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models](https://mlsanity.com/paper/2511.21759/) (2025)
- [Improving Variable-Length Generation in Diffusion Language Models via Length Regularization](https://mlsanity.com/paper/2602.07546/) (2026)
- [Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models](https://mlsanity.com/paper/2604.08964/) (2026)
- [A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models](https://mlsanity.com/paper/2511.15098/) (2025)
- [dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models](https://mlsanity.com/paper/2603.18806/) (2026)
- [Consistent Diffusion Language Models](https://mlsanity.com/paper/2605.00161/) (2026)
- [GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference](https://mlsanity.com/paper/2609.17573/) (2026)
- [STDec: Spatio-Temporal Stability Guided Decoding for dLLMs](https://mlsanity.com/paper/2604.06330/) (2026)
- [DARE: Diffusion Language Model Activation Reuse for Efficient Inference](https://mlsanity.com/paper/2605.08134/) (2026)
- [Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs](https://mlsanity.com/paper/2605.18165/) (2026)
- [The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models](https://mlsanity.com/paper/2605.24697/) (2026)
- [Optimus: Elastic Decoding for Efficient Diffusion LLM Serving](https://mlsanity.com/paper/2605.24832/) (2026)
- [STaRR: Spatial-Temporal Token-Dynamics-Aware Responsive Remasking for Diffusion Language Models](https://mlsanity.com/paper/2601.04205/) (2025)
- [Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding](https://mlsanity.com/paper/2603.04514/) (2026)
- [Beyond Scattered Acceptance: Fast and Coherent Inference for DLMs via Longest Stable Prefixes](https://mlsanity.com/paper/2603.05454/) (2026)
- [Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models](https://mlsanity.com/paper/2605.14530/) (2026)

## Abstract (arXiv)

Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict all future suffix tokens at each step while retaining only a small fraction. We propose Diffusion Scratchpad (DPad), a training-free method that restricts attention to a small set of nearby suffix tokens, preserving fidelity while eliminating redundancy. DPad integrates two strategies: (i) a sliding window, which maintains a fixed-length suffix window, and (ii) distance-decay dropout, which deterministically removes distant suffix tokens before attention computation. This simple design is compatible with existing optimizations such as prefix caching and can be implemented with only a few lines of code. Comprehensive evaluations across multiple benchmarks on LLaDA-1.5 and Dream models demonstrate that DPad delivers up to 61.4× speedup over vanilla dLLMs while maintaining comparable accuracy, highlighting its potential for efficient and scalable long-sequence inference. Our code is available at https://github.com/Crys-Chen/DPad.