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 target response space, replacing prefix prompting. Evaluated on mathematical reasoning, code generation, and trip planning, TI improves performance by 9.40% and accelerates multi-token generation.

Summary generated by Claude — human-verified