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Aidar Myrzakhan

VILA Lab, MBZUAI

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Tianyi Li

VILA Lab, MBZUAI

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Bowei Guo

VILA Lab, MBZUAI

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Shengkun Tang

VILA Lab, MBZUAI

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References (32)

[1]
Diffusion Language Models are Super Data Learners
2025Jinjie Ni, Qian Liu et al.
[2]
Attention Sinks in Diffusion Language Models
2025Maximo Eduardo Rulli, Simone Petruzzi et al.
[3]
A Survey on Diffusion Language Models
2025Tianyi Li, Mingda Chen et al.
[4]
Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding
2025Chengyue Wu, Hao Zhang et al.
[5]
MMaDA: Multimodal Large Diffusion Language Models
2025Ling Yang, Ye Tian et al.
[6]
Why do LLMs attend to the first token?
2025Federico Barbero, 'Alvaro Arroyo et al.
[7]
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
2025Marianne Arriola, Aaron Gokaslan et al.
[8]
Wanda++: Pruning Large Language Models via Regional Gradients
2025Yifan Yang, Kai Zhen et al.
[9]
When Attention Sink Emerges in Language Models: An Empirical View
2024Xiangming Gu, Tianyu Pang et al.
[10]
Simple and Effective Masked Diffusion Language Models
2024S. Sahoo, Marianne Arriola et al.
[11]
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
2023David Rein, Betty Li Hou et al.
[12]
Efficient Streaming Language Models with Attention Sinks
2023Guangxuan Xiao, Yuandong Tian et al.
[13]
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
2023Elias Frantar, Dan Alistarh
[14]
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
2022Zhengfu He, Tianxiang Sun et al.
[15]
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
2022Shansan Gong, Mukai Li et al.
[16]
Diffusion-LM Improves Controllable Text Generation
2022Xiang Lisa Li, John Thickstun et al.
[17]
Structured Denoising Diffusion Models in Discrete State-Spaces
2021Jacob Austin, Daniel D. Johnson et al.
[18]
Measuring Massive Multitask Language Understanding
2020Dan Hendrycks, Collin Burns et al.
[19]
PIQA: Reasoning about Physical Commonsense in Natural Language
2019Yonatan Bisk, Rowan Zellers et al.
[20]
Importance Estimation for Neural Network Pruning
2019Pavlo Molchanov, Arun Mallya et al.

Showing 20 of 32 references

Founder's Pitch

"Revolutionize Diffusion Language Models with Sink-Aware Pruning for efficient inference."

Model OptimizationScore: 5View PDF ↗

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Why It Matters

This research provides a novel pruning strategy specifically tailored for Diffusion Language Models (DLMs), allowing significant reduction in inference cost by targeting unstable attention sinks. Without such optimizations, deploying DLMs at scale can be computationally expensive and inefficient.

Product Angle

Create a tool or library that applies Sink-Aware Pruning to existing Diffusion Language Models, improving their efficiency with minimal setup and integration hurdles.

Disruption

The approach can render many existing model optimization techniques obsolete for Diffusion Language Models by providing a specifically tailored solution that enhances inference efficiency significantly without retraining.

Product Opportunity

As AI models grow in complexity, tools that offer significant computational savings—like this pruning strategy—are valuable to industries that rely on large-scale language models, such as cloud services and AI startups.

Use Case Idea

Develop a plug-in for AI-based text generation tools that automatically optimizes any diffusion-based language model for faster inference by pruning transient attention sinks.

Science

The paper introduces Sink-Aware Pruning, a method to optimize Diffusion Language Models by identifying and pruning transient attention sinks—tokens that attract inconsistent attention spans across multiple timesteps. Unlike traditional AR models, where attention 'sink' tokens are stable, DLMs have shifting sinks due to the nature of their iterative denoising process, making existing pruning strategies less effective.

Method & Eval

The approach was evaluated by applying the Sink-Aware Pruning to DLMs and comparing it against strong prior pruning baselines. It showed improved quality-efficiency trade-offs, suggesting it's able to maintain performance while reducing computational load.

Caveats

The effectiveness of this pruning method may vary depending on the specific architecture of the diffusion model being used. Furthermore, the integration and customization for various models could require adaptations depending on unique attention sink behaviors.

Author Intelligence

Aidar Myrzakhan

VILA Lab, MBZUAI

Tianyi Li

VILA Lab, MBZUAI

Bowei Guo

VILA Lab, MBZUAI

Shengkun Tang

VILA Lab, MBZUAI

Zhiqiang Shen

VILA Lab, MBZUAI