Algorithm design for large-scale Vehicle Routing Problems (VRPs) is computationally expensive, making it crucial to improve training efficiency without degrading solution quality. Recent work has shown that embedding curriculum learning into a Genetic Programming Guided Local Search (GPGLS) framework can reduce training time. However, existing methods rely on manually specified curriculum schedules with fixed phase lengths, which require extensive trial-and-error tuning and limit transfer to new problem settings. We propose an Adaptive Curriculum Learning GPGLS that automatically controls curriculum progression by monitoring the structural stability of the generation-best GP tree and the saturation of population-level fitness improvement, triggering stage transitions when further progress in the current phase is unlikely. Experimental results on large-scale VRP benchmarks show that the proposed strategy reduces training time by about 10% on average while maintaining comparable or better final solution quality than both fixed-schedule curriculum and non-curriculum baselines.