Unlocking the Potential of Diffusion Language Models through Template Infilling

📰 ArXiv cs.AI

Diffusion Language Models can be improved with Template Infilling, a new conditioning methodology

advanced Published 8 Apr 2026

Action Steps

Understand the limitations of prefix-based prompting in Diffusion Language Models
Implement Template Infilling to align structural anchors across the target response space
Evaluate the performance of DLMs with TI on various language tasks
Fine-tune DLMs with TI for specific applications and datasets

Who Needs to Know This

ML researchers and AI engineers can benefit from this approach to enhance language model performance, particularly in tasks requiring flexible and structured responses

Key Insight

💡 Template Infilling can unlock the full potential of Diffusion Language Models by providing a more flexible and structured conditioning methodology