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The paper introduces Wasserstein-Tilted Flow Maps (WTF), a simulation-free reinforcement learning algorithm for fine-tuning flow-based generative models to enhance reward alignment, achieving higher rewards with up to 280x less compute than baselines.
This paper introduces PMOT, a potential-flow framework for general p-cost optimal transport using continuous normalizing flows, with theoretical zero-loss exactness and promising results on synthetic and high-dimensional benchmarks.