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Graph-based diffusion models have shown promising results in terms ofgenerating high-quality solutions to NP-complete (NPC) combinatorialoptimization (CO) problems. However, those models are often inefficient ininference, due to the iterative evaluation nature of the denoising diffusionprocess. This paper proposes to use progressive distillation to speed up theinference by taking fewer steps (e.g., forecasting two steps ahead within asingle step) during the denoising process. Our experimental results show thatthe progressively distilled model can perform inference 16 times faster withonly 0.019% degradation in performance on the TSP-50 dataset.

Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation