iterative refinement

**Iterative Refinement** in text generation is a **strategy where the model generates an initial output and then repeatedly refines it through multiple passes** — each iteration improves upon the previous output by correcting errors, filling in masked positions, or adjusting token choices, converging toward a high-quality final result. **Iterative Refinement Methods** - **Mask-Predict**: Mask the least confident tokens from the previous iteration — re-predict them conditioned on the rest. - **CMLM (Conditional Masked Language Model)**: Ghazvininejad et al. — iteratively unmask tokens from a fully masked initial sequence. - **Edit-Based**: Identify and modify specific positions — insertions, deletions, and replacements. - **Denoising**: Add noise to the previous output and denoise — each iteration removes more noise. **Why It Matters** - **Quality Recovery**: Recovers much of the quality gap between non-autoregressive and autoregressive models. - **Adaptive Compute**: More iterations = better quality — can stop early for speed or continue for quality. - **Flexible**: Works with various base architectures — Transformer, diffusion models, or edit-based models. **Iterative Refinement** is **draft and polish** — generating an initial output and progressively improving it through multiple correction passes.