Title: Soft-Di[M]O: Improved one-step Image Discrete Model
Primary area: generative models
Keywords: Diffusion Distillation; Discrete Diffusion; Mask Diffusion; One-step Generation

Abstract: One-step generators distilled from Masked Diffusion Models (MDMs) compress multiple sampling steps into a single forward pass, enabling efficient text and image synthesis. 
However, they suffer two key limitations: they inherit modeling bias from the teacher, and their discrete token outputs block gradient flow, preventing post-distillation refinements such as adversarial training, reward-based fine-tuning, and Test-Time Embedding Optimization (TTEO). 
In this work, we introduce soft embeddings, a simple relaxation that replaces discrete tokens with the expected embeddings under the generator’s output distribution. 
Soft embeddings preserve representation fidelity for one-step discrete generator while 
providing a fully differentiable continuous surrogate that is compatible with teacher backbones and tokenizer decoders.
Integrating soft embeddings into the Di[M]O distillation framework (denoted Soft-Di[M]O) makes one-step generators end-to-end trainable and enables straightforward application of GAN-based refinement, differentiable reward fine-tuning, and TTEO. 
Empirically, across multiple MDM teachers (e.g., MaskBit, MaskGen), Soft-Di[M]O achieves state-of-the-art one-step results: improved class-to-image performance, a one-step FID of 1.56 on ImageNet-256 with GAN-based refinement, along with higher GenEval and HPS scores on text-to-image with reward fine-tuning, and further gains from TTEO.