Edit-Based Refinement for Parallel Masked Diffusion Language Models
Introduction
ME-DLM is a lightweight edit-based refinement framework for masked diffusion language models. It first generates a complete response through parallel diffusion decoding, then refines the output with minimal edit operations such as replacement, deletion, and insertion, conditioned on the full sequence. By using edit distance as deterministic training supervision, ME-DLM improves sequence-level consistency while preserving the decoding efficiency of diffusion models. Built on LLaDA, it achieves consistent gains on HumanEval and GSM8K while using only one-eighth of the total diffusion steps.
Models
Acknowledgments
We thank the following amazing projects that truly inspired us:
