d3LLM/d3LLM_LLaDA

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

This repository contains d3LLM-LLaDA, an ultra-fast diffusion language model presented in the paper d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation.

• —📄 Paper: arXiv:2601.07568
• —💻 Code: GitHub - hao-ai-lab/d3LLM
• —🌐 Blog: Ultra-Fast Diffusion LLMs
• —🕹️ Demo: d3LLM Demo

Model Description

d3LLM-LLaDA is an ultra-fast diffusion language model that strikes a balance between accuracy and parallelism. It uses pseudo-trajectory distillation to teach the model which tokens can be decoded confidently at early steps, and employs an entropy-based multi-block decoding mechanism with KV-cache refresh during inference.

Key Features

• —🚀 High throughput: 5.0× faster than autoregressive models (Qwen-2.5-7B-it) on H100 GPU and 3.5× faster on A100 GPU.
• —📊 High AUP: Achieves high Accuracy Under Parallelism scores across benchmarks.
• —🔧 Task Optimization: Specifically optimized for coding and math reasoning tasks.

Installation

To use this model, it is recommended to clone the official repository and install the required dependencies:

# Clone the repository
git clone https://github.com/hao-ai-lab/d3LLM.git
cd d3LLM

# Install dependencies
pip install -r requirements.txt

Citation

If you find d3LLM useful for your research, please cite the following work:

@inproceedings{ICML'26:d3llm,
    title = {d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation},
    author = {Yu-Yang Qian and Junda Su and Lanxiang Hu and Peiyuan Zhang and Zhijie Deng and Peng Zhao and Hao Zhang},
    booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
    pages = {to appear},
    year = {2026}
}