Dep2C: Dependency-Aware Credit Assignment and Coordinated Decoding in Masked Image Generation

Masked generative models (MGMs) enable efficient text-to-image generation through iterative parallel decoding. However, coarse-grained credit assignment may overlook the unequal downstream importance of committed tokens, while confidence-based decoding may jointly commit strongly dependent tokens. We propose Dep2C, a dependency-aware framework for fine-grained credit assign- ment and coordinated decoding. During RL post-training, Dep2C-Credit traces dependencies from future, prompt-relevant predictions back to previously com- mitted tokens and accumulates them along the generation trajectory to estimate to- ken importance. These estimates guide hierarchical credit allocation across decod- ing steps and individual tokens. During inference, Dep2C-Decoding combines prediction confidence and dependency degree to prioritize candidates, while using pairwise dependencies to constrain joint commitment. Experiments on Show-o and Lumina-DiMOO demonstrate improvements in both compositional genera- tion and complex prompt following, achieving GenEval scores of 0.86 and 0.92 and DPG-Bench scores of 84.37 and 83.69, respectively.