New paper 🎉: Coupling Models for One-Step Discrete Generation What is the right route to one-step discrete generation? Recently, the default answer has been: make discrete data continuous, learn a flow map, and then decode back to discrete samples. This is a natural route, but it is not the only one. We propose a different perspective: instead of forcing discrete generation through a continuous flow map, we learn a coupling between discrete sequences and Gaussian noise. Then we train a one-step generator that directly predicts the sequence from the noise using cross-entropy. At inference time, generation is simple: sample Gaussian noise → decode once → obtain a discrete sample. No autoregressive loop. No iterative denoising trajectory. No learning continuous flow model. The key idea is that, for one-step discrete generation, the data–noise coupling should not be treated as a fixed background choice. It is the central object to learn. Empirically, Coupling Models improve strong one-step baselines across text generation, biological sequence generation, and binary image generation. Paper: arxiv.org/pdf/2605.07193 Code: github.com/pengzhangzhi/Coup… Work done with @bose_joey @Anru_Zhang @AlexanderTong7