UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models

Uniform Discrete Diffusion (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains largely unexplored. We observe that naively adapting GRPO to UDM leads to unstable training and marginal performance. To address this, we propose , the first framework that integrates UDM with RL. Our method is guided by two key insights: (i) treating the final clean sample, rather than intermediate predicted sample, as the action provides more accurate and stable optimization signals; and (ii) adopting the forward process to reconstruct the training trajectories helps the model learn probability paths that are more consistent with pretraining. For efficiency, we introduce Reduction-Step and CFG-Free training strategies. significantly improves the performance of the base model across multiple T2I tasks. Notably, GenEval accuracy improves from $69\%$ to $96\%$ and PickScore increases from $20.46$ to $23.81$, achieving state-of-the-art performance in both continuous and discrete settings. On the OCR benchmark, accuracy improves from $4\%$ to $57\%$, further validating the effectiveness and generalization capability of our method.