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[2602.00737] Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization
Summary
This article presents a novel framework called Pareto-Conditioned Diffusion (PCD) for offline multi-objective optimization, addressing challenges in generalizing beyond observed data by conditioning on desired trade-offs.
Why It Matters
The research is significant as it introduces a new method for optimizing multiple objectives in scenarios where only limited data is available. This has practical implications in various fields, including engineering and economics, where decision-making often involves balancing competing objectives.
Key Takeaways
- PCD formulates offline multi-objective optimization as a conditional sampling problem.
- The framework avoids explicit surrogate models, enhancing efficiency.
- PCD employs a reweighting strategy to focus on high-performing samples.
- Experiments show PCD achieves competitive performance across various tasks.
- Greater consistency in results compared to existing offline MOO approaches.
Computer Science > Machine Learning arXiv:2602.00737 (cs) [Submitted on 31 Jan 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization Authors:Jatan Shrestha, Santeri Heiskanen, Kari Hepola, Severi Rissanen, Pekka Jääskeläinen, Joni Pajarinen View a PDF of the paper titled Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization, by Jatan Shrestha and 5 other authors View PDF HTML (experimental) Abstract:Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where only a static dataset is available, the main challenge is generalizing beyond observed data. We introduce Pareto-Conditioned Diffusion (PCD), a novel framework that formulates offline MOO as a conditional sampling problem. By conditioning directly on desired trade-offs, PCD avoids the need for explicit surrogate models. To effectively explore the Pareto front, PCD employs a reweighting strategy that focuses on high-performing samples and a reference-direction mechanism to guide sampling towards novel, promising regions beyond the training data. Experiments on standard offline MOO benchmarks show that PCD achieves highly competitive performance and, importantly, demonstrates greater consistency across diverse tasks than existing offline MOO approaches. Comments: Subjects: Machine Learning (cs.LG); ...
