Diffusion language models conventionally depend on a hand-designed forward corruption process, which constrains what the model can learn to undo. We introduce FReDA, a diffusion-based language model that eliminates the prescribed forward process entirely. Instead, language modeling is framed as recursive distribution refinement, in which model-generated drafts serve as implicit intermediate states — and training avoids backpropagation through time across the refinement loop. Text is produced by iterative refinement, either through self-refinement or best-of-N selection among parallel candidates. At sub-8B scale, FReDA-4B achieves up to 15% gains on reasoning and coding tasks over larger diffusion models while delivering 1.5-1.8x inference speedups over diffusion baselines.