Reward‑maximization alignment methods for discrete diffusion models mainly steer the reverse process by tweaking token logits or picking favorable intermediate sequences. These approaches treat inference as a one‑way pass and lack a mechanism to revisit undesirable token choices.\ \ We introduce Spectral Feedback, an algorithm that selects edit positions in a feedback loop, allowing the model to iteratively correct its own generations. The method exploits the mask structure of discrete diffusion models by re‑masking and re‑sampling tokens, analogous to image‑editing techniques that re‑inject noisy latents and run the reverse process again.\ \ Instead of focusing on which token labels to assign for maximizing a reward, Spectral Feedback treats which tokens to revisit as the central alignment problem. Selecting edit positions is challenging because edit effects are interdependent: the impact of changing one token depends on which others are edited simultaneously. We define an edit‑set as a collection of token positions to re‑mask and re‑sample. Inspired by sparse interactions in biological systems, we empirically find that edit‑set value functions for protein inverse folding admit sparse Fourier representations: $$ V(S) \approx \sum_{k\in\mathcal{K}} a_k \cos(\omega_k \cdot S) $$ where $S$ denotes the edit‑set and $\mathcal{K}$ contains only a few non‑zero frequencies. This sparsity enables Spectral Feedback to efficiently learn and optimize the value function, quickly identifying the most valuable edit positions.\ \ Spectral Feedback is model‑agnostic and can be applied to pretrained, test‑time aligned, and RL‑fine‑tuned diffusion models without altering the underlying generative process. In experiments on a protein stability reward oracle, the algorithm improves stable‑protein rates by 32.3% for a pretrained model, 24.8% for Best‑of‑10, and 5.8% for a state‑of‑the‑art RL‑fine‑tuned diffusion model.\ \ Review