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FlowBudget 提出一种免训练的采样预算估计方法，通过仿真自由的插值探针估计求解器相对的采样复杂度，从而在不进行 FID-vs-NFE 扫描的情况下确定流匹配模型所需的函数求值次数（NFE）。
GenPO++ proposes a reversible generative policy optimization framework that uses history states as auxiliary memory in a high-order reversible ODE solver, enabling exact inversion and Jacobian-free likelihood-ratio computation for flow-based policies in reinforcement learning. It achieves competitive performance on large-scale control, fine-tuning, and real-world robotic tasks while improving stability and efficiency.