Post-training reasoning models is computationally expensive, and the allocation between supervised fine-tuning (SFT) and reinforcement learning (RL) substantially affects reasoning quality. Existing methods operate at the training-procedure level and treat the reasoning corpus as homogeneous, ignoring the structural heterogeneity of individual traces. We propose GROVE (Graph-based Routing Over Variant Experts), an adaptive framework that routes each example to SFT or RL based on the structural topology of its reasoning trace. GROVE parses each trace into a Reasoning Tree, scores its complexity via graph entropy, and partitions the corpus through a data-driven Gaussian Mixture Model with no hand-tuned threshold. Low-complexity traces are trained with SFT and high-complexity traces with GRPO as parallel LoRA adapters from a shared frozen base, merged into a single deployable model with no inference-time overhead. Across two training corpora and five reasoning benchmarks, GROVE consistently outperforms single-stage and naive-hybrid baselines, and a reversed-routing ablation confirms that this mapping is intrinsic rather than arbitrary.