#Sink‑Aware Pruning#Diffusion Language Model#Attention Sink#Iterative Denoising#Model Pruning#Stable Global Anchor#Inference Cost#Non‑Retraining Pruning#ArXiv 2602.17664#cs.CL#cs.AI#cs.LG
📌 Key Takeaways
Diffusion language models (DLMs) are expensive to run because each generation step involves iterative denoising.
Traditional pruning heuristics from autoregressive LLMs keep stable attention‑sink tokens; however, DLM sinks fluctuate significantly across timesteps.
The paper introduces Sink‑Aware Pruning, an algorithm that detects and removes unstable sink tokens during inference.
The approach is non‑intrusive—no retraining or fine‑tuning is required—and still achieves better quality‑efficiency balance than prior methods.
Experimental results on standard benchmarks show that Sink‑Aware Pruning surpasses strong baselines under the same compute budget.
All code and data are publicly available on a hosted repository for reproducibility.
📖 Full Retelling
This research, conducted by Aidar Myrzakhan, Tianyi Li, Bowei Guo, Shengkun Tang, and Zhiqiang Shen and submitted on 19 February 2026, investigates a novel pruning technique for diffusion language models, coined **Sink‑Aware Pruning**. The authors identify that, unlike in autoregressive language models, the attention‑sink positions in diffusion models are highly volatile throughout the denoising trajectory, rendering them less structurally essential. Leveraging this observation, they propose automatically detecting and eliminating these unstable sinks without requiring retraining. The method demonstrates a superior quality‑efficiency trade‑off and outperforms strong baselines when matched for computation, providing free code on the authors’ GitHub repository.
🏷️ Themes
Machine Learning, Natural Language Processing, Model Compression, Diffusion Models, Inference Efficiency
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Deep Analysis
Why It Matters
Sink-aware pruning reduces inference cost of diffusion language models by targeting unstable attention sinks, improving the quality-efficiency trade‑off without retraining. This enables faster, cheaper deployment of DLMs in real‑world applications.
Context & Background
Diffusion language models use iterative denoising, causing high inference cost.
Traditional pruning keeps attention sink tokens, assuming they are stable anchors.
The new study shows sink positions vary across timesteps, making them less essential.
What Happens Next
Researchers will test the pruning method on larger DLMs and integrate it into existing pipelines. The technique may prompt re‑evaluation of pruning heuristics for other generative models.
Frequently Asked Questions
What is a sink token?
A token that attracts attention from many other tokens, often used as a stable reference point in language models.
Does the pruning require retraining?
No, the method prunes unstable sinks without any additional training, preserving model performance.
Original Source
--> Computer Science > Computation and Language arXiv:2602.17664 [Submitted on 19 Feb 2026] Title: Sink-Aware Pruning for Diffusion Language Models Authors: Aidar Myrzakhan , Tianyi Li , Bowei Guo , Shengkun Tang , Zhiqiang Shen View a PDF of the paper titled Sink-Aware Pruning for Diffusion Language Models, by Aidar Myrzakhan and Tianyi Li and Bowei Guo and Shengkun Tang and Zhiqiang Shen View PDF HTML Abstract: Diffusion Language Models incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that this assumption does not hold for DLMs: the attention-sink position exhibits substantially higher variance over the full generation trajectory (measured by how the dominant sink locations shift across timesteps), indicating that sinks are often transient and less structurally essential than in AR models. Based on this observation, we propose ${\bf \texttt{Sink-Aware Pruning}}$, which automatically identifies and prunes unstable sinks in DLMs (prior studies usually keep sinks for AR LLMs). Without retraining, our method achieves a better quality-efficiency trade-off and outperforms strong prior pruning baselines under matched compute. Our code is available at this https URL . Comments: Code at: this https URL Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.17664 [cs.CL] (or arXiv:2602.17664v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.17664 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhiqiang Shen [ view email ] [v1] Thu, 19 Feb 2026 18:59:50 UTC (311 KB) Full-text links: Access Paper: View a PDF of the paper titled Sink-Aware Pruning for Diffusion Language Models, by Aidar Myrzakhan and Tianyi Li and Bowei Guo and Shengkun Tang and ...
