Scaling test-time computation in Multimodal Large Language Models (MLLMs) enforces heavy visual analysis and long logical reasoning uniformly across queries, leading to severe token waste and overthinking. We propose **Ada-DeVL**, a framework that decouples visual perception depth from logical reasoning intensity, allowing the model to adaptively select one of four visuo-reasoning pathways according to problem complexity. To learn this adaptive behavior, we first apply a Self-Distillation Supervised Fine-Tuning (SFT) stage that generates difficulty-aligned, hallucination-reduced data while avoiding the exposure bias of cross-model distillation. We then use Group Relative Policy Optimization (GRPO) with a composite reward, incorporating diversity and format-adherence constraints to prevent mode collapse and shortcut exploitation. Notably, on the MMMU benchmark, the Qwen2.5-VL-7B based Ada-DeVL achieves **54.0%** accuracy, surpassing Vision-SR1 while saving **39.6%** tokens; the Qwen3-VL-8B based model reaches **64.0%**, outperforming its vanilla counterpart and reducing token consumption by **44.6%**, and achieves competitive accuracy against the strong Qwen3-VL-8B-Thinking baseline with significantly lower token costs (averaging 1560.8 vs. 4889.0 tokens).