Advantage-weighted conditional optimal transport flow matching simultaneously steers flow policies toward high-value actions and straightens their integration paths, enabling 2-3 step inference while improving task success.
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Optimal Transport Q-Learning for Flow Policy Steering and Acceleration
Advantage-weighted conditional optimal transport flow matching simultaneously steers flow policies toward high-value actions and straightens their integration paths, enabling 2-3 step inference while improving task success.