ParetoPilot: Zero-surrogate diffusion model beats 14 baselines on 51 tasks
No surrogate model needed: ParetoPilot uses diffusion priors to outperform traditional MOO methods dramatically.
A team of researchers has proposed ParetoPilot, a novel framework for offline multi-objective optimization (MOO) that eliminates the need for surrogate models. Traditional MOO methods rely on external proxies to evaluate candidate designs, which introduces computational overhead, risks deceptive evaluations, and requires joint training with generative models. ParetoPilot sidesteps these issues by fully leveraging the conditional priors already embedded within pre-trained diffusion models.
At the core of ParetoPilot is the Infer-Perturb-Guide (IPG) engine, seamlessly interleaved into the diffusion model's reverse denoising steps. First, it infers the instantaneous objective direction by comparing conditional and unconditional noise predictions. Then, it orthogonalizes a parallel gravity field for strict convergence and an edgeness-aware repulsive force for mutual diversity, creating a dynamically annealed perturbation vector. Finally, this target guides generation via standard Classifier-Free Guidance. Across 51 benchmark tasks, ParetoPilot outperformed 14 state-of-the-art surrogate-based and inverse generative baselines, achieving superior hypervolume and robust Pareto front coverage. This zero-surrogate approach also enhances data privacy by eliminating the need to train auxiliary models on sensitive design data.
- Eliminates external surrogate models, reducing computational cost and avoiding deceptive evaluation pitfalls.
- Uses a novel Infer-Perturb-Guide (IPG) engine within diffusion denoising for implicit objective inference and diversity maintenance.
- Outperforms 14 SOTA baselines on 51 tasks with improved hypervolume and Pareto front coverage.
Why It Matters
Faster, more robust multi-objective design optimization without expensive simulations or compromising data privacy.