[2511.17812] Score-Regularized Joint Sampling with Importance Weights for Flow Matching

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.17812 (cs) [Submitted on 21 Nov 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Score-Regularized Joint Sampling with Importance Weights for Flow Matching Authors:Xinshuang Liu, Runfa Blark Li, Shaoxiu Wei, Truong Nguyen View a PDF of the paper titled Score-Regularized Joint Sampling with Importance Weights for Flow Matching, by Xinshuang Liu and 3 other authors View PDF HTML (experimental) Abstract:Flow matching models effectively represent complex distributions, yet estimating expectations of functions of their outputs remains challenging under limited sampling budgets. Independent sampling often yields high-variance estimates, especially when rare but high-impact outcomes dominate the expectation. We propose a non-IID sampling framework that jointly draws multiple samples to cover diverse, salient regions of a flow matching model's generative distribution. To balance diversity and quality, we introduce a score-based regularization for the diversity mechanism (SR), which uses the score function, i.e., the gradient of the log probability, to ensure samples are pushed apart within high-density regions of the data manifold, mitigating off-manifold drift. To enable unbiased estimation when desired, we further develop an approach for importance weighting of non-IID flow samples by learning a residual velocity field that reproduces the marginal distribution of the non-IID samples and by evolvin...