Iterative Refinement

Iterative refinement studies how a model can improve a solution over multiple steps instead of producing it in one pass, which can help resolve errors and use additional computation at inference time. Common approaches include recurrent or weight-shared networks, residual and multiscale correction, diffusion or flow-based updates, and generate–evaluate–revise loops with external feedback. These methods appear in reasoning, image generation and segmentation, 3D reconstruction, scientific simulation, and inverse problems, and are evaluated on tasks ranging from Sudoku and image benchmarks to turbulent-flow reconstruction. A central tension is that more steps can improve accuracy or detail, but add cost and do not guarantee progress: results depend on whether the feedback reliably identifies errors and whether simpler one-shot methods are already sufficient.