Generative Modeling by Value-Driven Transport

• URL: http://arxiv.org/abs/2605.22507v1
• Date: Thu, 21 May 2026 13:57:06 GMT
• Title: Generative Modeling by Value-Driven Transport
• Abstract summary: We develop a new framework for generative modeling based on a discrete-time control formulation of measure transport.<n>We show that well-trained VDT policies enjoy numerous favorable properties in comparison with other state-of-the-art methods.
• Score: 14.31847187460321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic results from control theory, we formulate our problem as a linear program whose dual variables correspond to the \emph{optimal value function} of the control problem, which directly encodes the optimal control policy. Exploiting this LP formulation, we develop an efficient simulation-free primal-dual algorithm for computing approximately optimal value functions and the associated \emph{value-driven transport} (VDT) policies which approximate the true optimal policy. We show that well-trained VDT policies enjoy numerous favorable properties in comparison with other state-of-the-art methods based on flows, diffusions, or Schrödinger bridges: they lead to straight transport paths which can be simulated quickly and robustly, and can be enhanced in all the same ways as diffusion and flow-based models (e.g., conditional generation, classifier-free guidance, unpaired data-to-data translation are all easy to incorporate). We evaluate our methodology in a range of experiments, with results that indicate strong performance and good potential for scalability.

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