# d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

arXiv: https://arxiv.org/abs/2601.07568 · HTML: https://mlsanity.com/paper/2601.07568/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion LLM and MoE inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 54 of its 65 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- Efficient LLM inference and serving / [Diffusion LLM and MoE inference](https://mlsanity.com/inference/diffusion-llm-and-moe-inference/)

## Builds on (most cited first, 30 of 54)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [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)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Solving Quantitative Reasoning Problems with Language Models](https://mlsanity.com/paper/2206.14858/) (2022)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Simple and Effective Masked Diffusion Language Models](https://mlsanity.com/paper/2406.07524/) (2024)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Simplified and Generalized Masked Diffusion for Discrete Data](https://mlsanity.com/paper/2406.04329/) (2024)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Dream 7B: Diffusion Large Language Models](https://mlsanity.com/paper/2508.15487/) (2025)
- [Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding](https://mlsanity.com/paper/2505.22618/) (2025)
- [Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data](https://mlsanity.com/paper/2406.03736/) (2024)
- [EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test](https://mlsanity.com/paper/2503.01840/) (2025)
- [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://mlsanity.com/paper/2402.02057/) (2024)
- [LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models](https://mlsanity.com/paper/2505.19223/) (2025)
- [MMaDA: Multimodal Large Diffusion Language Models](https://mlsanity.com/paper/2505.15809/) (2025)
- [Mercury: Ultra-Fast Language Models Based on Diffusion](https://mlsanity.com/paper/2506.17298/) (2025)
- [Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference](https://mlsanity.com/paper/2508.02193/) (2025)
- [LLaDA2.0: Scaling Up Diffusion Language Models to 100B](https://mlsanity.com/paper/2512.15745/) (2025)
- [Llama-Nemotron: Efficient Reasoning Models](https://mlsanity.com/paper/2505.00949/) (2025)
- [REST: Retrieval-Based Speculative Decoding](https://mlsanity.com/paper/2311.08252/) (2023)
- [dKV-Cache: The Cache for Diffusion Language Models](https://mlsanity.com/paper/2505.15781/) (2025)
- [Fast-dLLM v2: Efficient Block-Diffusion LLM](https://mlsanity.com/paper/2509.26328/) (2025)
- [The Diffusion Duality](https://mlsanity.com/paper/2506.10892/) (2025)

## Built on by (most cited first, 30 of 37)

- [DFlash: Block Diffusion for Flash Speculative Decoding](https://mlsanity.com/paper/2602.06036/) (2026)
- [Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding](https://mlsanity.com/paper/2607.05722/) (2026)
- [DMax: Aggressive Parallel Decoding for dLLMs](https://mlsanity.com/paper/2604.08302/) (2026)
- [JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting](https://mlsanity.com/paper/2606.18394/) (2026)
- [Improved Large Language Diffusion Models](https://mlsanity.com/paper/2606.25331/) (2026)
- [DAWN: Dependency-Aware Fast Inference for Diffusion LLMs](https://mlsanity.com/paper/2602.06953/) (2026)
- [Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs](https://mlsanity.com/paper/2601.08198/) (2026)
- [TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration](https://mlsanity.com/paper/2602.08404/) (2026)
- [LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning](https://mlsanity.com/paper/2603.13319/) (2026)
- [Learning from the Self-future: On-policy Self-distillation for dLLMs](https://mlsanity.com/paper/2606.18195/) (2026)
- [ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion](https://mlsanity.com/paper/2604.09450/) (2026)
- [AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters](https://mlsanity.com/paper/2607.19223/) (2026)
- [Few-Step Diffusion Language Models via Trajectory Self-Distillation](https://mlsanity.com/paper/2602.12262/) (2026)
- [Multi-Block Diffusion Language Models](https://mlsanity.com/paper/2606.29215/) (2026)
- [TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding](https://mlsanity.com/paper/2607.03236/) (2026)
- [OPTD: On-Policy Transition Distillation with Consistency-Guided Adaptive Compression for Few-Step Diffusion Language Models](https://mlsanity.com/paper/2608.02942/) (2026)
- [A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs](https://mlsanity.com/paper/2603.07475/) (2026)
- [TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models](https://mlsanity.com/paper/2604.00666/) (2026)
- [TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM](https://mlsanity.com/paper/2605.09536/) (2026)
- [Generalized Discrete Diffusion with Self-Correction](https://mlsanity.com/paper/2603.02230/) (2026)
- [Supportive Token Revealing for Fast Diffusion Language Model Decoding](https://mlsanity.com/paper/2606.04236/) (2026)
- [Enabling KV Caching of Shared Prefix for Diffusion Language Models](https://mlsanity.com/paper/2606.07571/) (2026)
- [Efficient On-Device Diffusion LLM Inference with Mobile NPU](https://mlsanity.com/paper/2606.13740/) (2026)
- [PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models](https://mlsanity.com/paper/2606.19534/) (2026)
- [CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models](https://mlsanity.com/paper/2607.24763/) (2026)
- [CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing](https://mlsanity.com/paper/2608.13925/) (2026)
- [AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding](https://mlsanity.com/paper/2606.08411/) (2026)
- [Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning](https://mlsanity.com/paper/2607.02963/) (2026)
- [Accelerating Diffusion Language Models via Structured Suffix Modeling](https://mlsanity.com/paper/2608.23167/) (2026)
- [CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models](https://mlsanity.com/paper/2609.13060/) (2026)

## Abstract (arXiv)

Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-order generation. However, realizing these benefits in practice is non-trivial, as dLLMs inherently face an accuracy-parallelism trade-off. Despite increasing interest, existing methods typically focus on only one-side of the coin, targeting either efficiency or accuracy. To address this limitation, we propose d3LLM (Pseudo-Distilled Diffusion Large Language Model), striking a balance between accuracy and parallelism: (i) during training, we introduce pseudo-trajectory distillation to teach the model which tokens can be decoded confidently at early steps, thereby improving parallelism; (ii) during inference, we employ entropy-based multi-block decoding with a KV-cache refresh mechanism to achieve high parallelism while maintaining accuracy. To better evaluate dLLMs, we also introduce AUP (Accuracy Under Parallelism), a new metric that jointly measures accuracy and parallelism. Experiments demonstrate that our d3LLM achieves up to 10× speedup over vanilla LLaDA/Dream, and 5× speedup over AR models without much accuracy drop. Our code is available at https://github.com/hao-ai-lab/d3LLM.