Archer reuses cached hidden states to speed up diffusion language models

Paper proposes Archer, a method that adaptively reuses cached hidden states during iterative refinement so diffusion language models (DLMs) can roll back earlier predictions at lower inference cost. DLMs revise predictions as context evolves, unlike irreversible autoregressive generation.