10 papers
Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition
Zhiqi Li, Wen Zhang, Bo Zhu
Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between noise and data. However, these mod…
A Few-Step Generative Model on Cumulative Flow Maps
Zhiqi Li, Duowen Chen, Yuchen Sun +1
We propose a unified, few-step generative modeling framework based on \emph{cumulative flow maps} for long-range transport in probability space, inspired by flow-map techniques for…
Generative Modeling with Orbit-Space Particle Flow Matching
Sinan Wang, Jinjin He, Shenyifan Lu +3
We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP is motivated by two insights:…
Trajectory Consistency for One-Step Generation on Euler Mean Flows
Zhiqi Li, Yuchen Sun, Duowen Chen +2
We propose \emph{Euler Mean Flows (EMF)}, a flow-based generative framework for one-step and few-step generation that enforces long-range trajectory consistency with minimal sampli…
An Impulse-formed Navier-Stokes Solver based on Long-range Particle Flow Maps
Zhiqi Li, Duowen Chen, Junwei Zhou +3
We present a particle-grid characteristic-mapping framework that extends long-range characteristic mapping from inviscid flows to general Navier-Stokes dynamics with viscosity, bod…
A Level Set Method on Particle Flow Maps
Jinjin He, Taiyuan Zhang, Zhiqi Li +3
This paper introduces a Particle Flow Map Level Set (PFM-LS) method for high-fidelity interface tracking. We store level-set values, gradients, and Hessians on particles concentrat…