# DiVR

Masked diffusion language models can remask generated text, but deciding where to revise by confidence or self-judgment often leaves the error in place. DiVR instead learns an outcome verifier on the generator's own decodes and, at each block boundary, judges whether the partial solution can still reach a correct answer, resampling the whole block when the score is low. On the supervised fine-tuned LLaDA-8B-Base generator, the five-benchmark math and code average rises from 35.3% to 43.4% at the cost of greedy decoding.

Masked diffusion language models can remask generated text, but deciding where to revise by confidence or self-judgment often leaves the error in place. DiVR instead learns an outcome verifier on the generator's own decodes and, at each block boundary, judges whether the partial solution can still reach a correct answer, resampling the whole block when the score is low. On the supervised fine-tuned LLaDA-8B-Base generator, the five-benchmark math and code average rises from 35.3% to 43.4% at the cost of greedy decoding.