Learning When to Trust: Risk-Adaptive Supervision for Flow Map Models

Flow map models (FMMs) achieve efficient one-step generation by directly learning finite-time transport, yet existing training strategies largely treat supervision reliability as globally uniform. We challenge this assumption and show that the relative reliability of different supervision signals can vary substantially across transport states. To formalize this observation, we develop a flow-map-aware risk framework that characterizes state-dependent supervision reliability and derives the optimal trade-off between complementary supervision sources. Building on this framework, we propose Risk-Adaptive Flow Map Learning (RAFL), which estimates state-dependent reliability from target-space disagreement and uses it to construct adaptive finite-time supervision. RAFL provides a lightweight post-training refinement for pretrained FMMs without modifying their inference procedure or introducing additional inference-time cost. Extensive experiments across diverse FMMs reveal substantial supervision heterogeneity and demonstrate consistent improvements over existing supervision strategies.