Unlocking the Potential of Diffusion Language Models through Template Infilling

In three linesTemplate Infilling (TI) is a conditioning methodology for Diffusion Language Models that aligns structural anchors across the entire response space, replacing prefix prompting. Evaluated on mathematical reasoning, code generation, and trip planning, TI achieves 9.40% improvements and accelerates multi-token generation.

Summary generated by Claude — human-verified