How Learning Dynamics Shape Decoding in Masked Diffusion Models

Diffusion Language Models have recently emerged as a promising alternative for reasoning, benefiting from bidirectional prediction and flexible token ordering at inference. However, how training shapes the usefulness of this flexibility remains unclear. In this paper, we study stage-wise learning dynamics in a masked diffusion model using a two-layer Transformer with a fixed output head on a graph-planning task. A local endpoint-first asymmetry under ordinary joint training motivates a framework separating endpoint preparation from predecessor learning. Within this framework, we establish a finite predecessor-risk decrease and derive risk-dependent reverse-circuit certificates. We derive conditions for endpoint reveal to bootstrap predecessor prediction and for top-probability and top-margin decoding to favor the reverse order, distinguishing sequence-level correctness from confidence-based selection. We also characterize order independence and confidence agreement when all mask-conditioned risks vanish. Together, our analysis relates stage-wise learning and internal computation to decoding preference and advantage.