Consolidating Reinforcement Learning for Multimodal Discrete Diffusion
  Models
Authors: name: Tianren Ma, name: Mu Zhang, name: Yibing Wang, name: Qixiang Ye

Optimizing discrete diffusion model (DDM) with rewards remains a challenge:
the non-autoregressive paradigm makes importance sampling intractable and
rollout complex, puzzling reinforcement learning methods such as Group Relative
Policy Optimization (GRPO). In this study, we introduce MaskGRPO, the first
viable approach to enable scalable multimodal reinforcement learning in
discrete diffusion with effective importance sampling and modality-specific
adaptations. To this end, we first clarify the theoretical foundation for DDMs,
which facilitates building an importance estimator that captures valuable token
fluctuation for gradient updates. We then delicately tailored the rollout
method for visual sequences, which yields diverse completions and reliable
optimization gradients. Upon math reasoning, coding, and visual generation
benchmarks, MaskGRPO brings more stable and efficient updates, leading to
stronger reasoning performance and better generation quality. This study
establishes MaskGRPO as a systematic policy optimization approach and the first
practical way for discretized visual diffusion.