DriftCO: A Drifting Model-Based Solver for Combinatorial Optimization

Diffusion model-based solvers have recently achieved strong performance in neural combinatorial optimization (CO). They generate candidates through stochastic denoising trajectories, which can provide both strong solution quality and useful candidate diversity. This makes them well suited to best-of-many pipelines, where multiple candidates are locally improved and the best solution is retained. However, multi-step denoising introduces substantial per-candidate inference cost. Recent acceleration methods distill the trajectory into fewer steps or even a single step, but such distillation can weaken the candidate diversity needed for effective best-of-many search. To mitigate this issue, we propose DriftCO, a drifting model-based one-step CO solver that promotes candidate quality and diversity through a training-time attraction–repulsion mechanism, rather than through long inference-time denoising trajectories. This mechanism encourages the model to favor high-quality solution regions while avoiding collapse into redundant search neighborhoods, allowing DriftCO to generate diverse candidates with strong solution quality in one step at inference time. Experiments on the traveling salesman problem and maximum independent set across multiple problem sizes demonstrate the effectiveness of DriftCO.