Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization

2026-01-31

AI Summary

This work addresses the challenge of generalizing to unseen Pareto-optimal solutions in offline multi-objective optimization, where reliance on static datasets often limits performance. The authors propose a Pareto-conditional diffusion framework that formulates the optimization problem as a diffusion sampling process conditioned on objective trade-offs, eliminating the need for explicit surrogate models. By incorporating a reweighting strategy and a reference direction guidance mechanism, the method effectively steers the sampling process toward high-quality regions of the Pareto front beyond the support of the training data distribution. Experimental results demonstrate that the proposed approach significantly outperforms existing methods on standard offline multi-objective benchmarks, achieving superior performance in terms of solution diversity, stability, and consistency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• multi-objective optimization
• offline optimization
• Pareto front
• generalization
• trade-offs

Innovation

• Pareto-Conditioned Diffusion
• offline multi-objective optimization
• conditional diffusion models
• Pareto front exploration
• reference-direction mechanism