We offer a simple modification to training continuous diffusion language models that leads to improvements in both accuracy and generation diversity on combinatorial reasoning benchmarks.

We improve continuous diffusion language models in the few-step regime by allowing them to correct previous integration errors, borrowing intuition from recursive reasoning models. On Sudoku hard this increases exact match accuracy from 38% to 72%.