Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed

📰 ArXiv cs.AI

Learn how to convert autoregressive language models to diffusion language models for faster generation while preserving task accuracy

advanced Published 1 May 2026

Action Steps

Identify a pretrained autoregressive language model to convert
Apply the AR-to-dLM conversion technique to transform the model into a diffusion language model
Evaluate the converted model's task accuracy and generation speed
Fine-tune the converted model if necessary to optimize performance
Deploy the efficient diffusion language model in a production environment

Who Needs to Know This

NLP engineers and researchers can benefit from this technique to improve the efficiency of their language models, and software engineers can apply this to develop more efficient language processing systems

Key Insight

💡 Converting autoregressive language models to diffusion language models can significantly improve generation speed while preserving task accuracy

Key Takeaways

Learn how to convert autoregressive language models to diffusion language models for faster generation while preserving task accuracy

Full Article

Title: Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed

Abstract:
arXiv:2512.14067v2 Announce Type: replace-cross Abstract: Diffusion language models (dLMs) have emerged as a promising paradigm that enables parallel, non-autoregressive generation, but their learning efficiency lags behind that of autoregressive (AR) language models when trained from scratch. To this end, we study AR-to-dLM conversion to transform pretrained AR models into efficient dLMs that excel in speed while preserving AR models' task accuracy. We achieve this by identifying limitations in

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