Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning

Consistency distillation has significantly accelerated diffusion-model inference, but its sampling dynamics remain underexplored. Wereveal an asymmetry: although Logit-Normal sampling priors work well forstandard iterative generation, consistency distillation exhibits a differentdifficulty profile (e.g., U-shaped), with optimization bottlenecks concen-trated at the boundary stages rather than intermediate steps. To addressthe limitations of static sampling under evolving learning demands, wepropose Curvature-Adaptive Consistency Flow Matching (CACFM). Byformulating distillation as a dynamic decision process, CACFM usesa lightweight reinforcement learning agent to probe Probability FlowODE trajectories and construct an efficiency-oriented curriculum thatprioritizes critical regions without manual scheduling. Combined withFlow-adapted DMD and adversarial consistency objectives, our RL-basedscheduler achieves state-of-the-art results on large-scale models suchas FLUX and SDXL, mitigating structural deformities and preservinghigh-frequency details in extreme few-step regimes.