Discrete diffusion drops offline training: DMBD optimizes discrete variables directly and beats sampling baselines in 14 of 15 multi-robot task-allocation settings
Discrete diffusion for combinatorial optimization has relied on an offline-trained neural denoiser, while training-free model-based diffusion works only on continuous variables and must round its solutions. DMBD replaces the denoising step with Monte Carlo sampling weighted by the known objective to approximate categorical reverse transitions directly; on a heterogeneous multi-robot coalition-formation and scheduling benchmark it reaches the lowest makespan among sampling-based methods in 14 of 15 settings and beats all baselines in 8.