ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
arXiv:2606.04468v1 Announce Type: new Abstract: Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions. While recent generative methods have achieved notable success, they predominantly rely on external surrogate models. This dependency introduces significant computational overhead, suffers from deceptive evaluations, and deviates from the prevailing paradigm of jointly training mainstream generative models with conditions. To address these bottlenecks, we propose ParetoPilot, a novel
The proliferation of advanced generative AI models necessitates more efficient and less resource-intensive optimization techniques, driving innovation in offline multi-objective optimization without relying on external surrogates.
This development allows for the discovery of novel designs from static datasets more efficiently, potentially accelerating R&D in various fields without the typical computational overhead or risk of deceptive evaluations.
Offline multi-objective optimization can now be performed with a zero-surrogate approach using diffusion models, reducing computational costs and improving the reliability of generative design processes.
- · AI researchers
- · Generative AI platforms
- · R&D intensive industries
- · Drug discovery
- · Developers reliant on surrogate models
- · Computationally intensive MOO methods
Accelerated design cycles for complex systems based on existing data.
Reduced barriers to entry for companies seeking to leverage advanced optimization due to lower computational requirements.
Potentially democratizes access to sophisticated design capabilities, fostering innovation across multiple sectors.
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Read at arXiv cs.LG