Reinforcement Learning (RL) has proven effective in eliciting reasoning abilities of Multimodal Large Language Models (MLLMs) and is emerging in document understanding. However, existing approaches usually lack fine-grained region-level grounding supervision, while the key challenge in document reasoning lies in linking visual evidence within precise regions directly to question–answer semantics. We introduce RegionDoc-R1, a novel framework for document understanding that enhances MLLMs’ reasoning with step-wise feedback. Instead of previous OCR-based text positions, we encourage the model to predict question-relevant Regions of Interest (RoIs) and ground the answer in these regions. Specifically, we first propose the Region-Aware Group Relative Policy Optimization (RA-GRPO), which encourages models to utilize region-level spatial information in documents for reasoning. Correspondingly, we construct a hybrid training corpus, named SR-Doc, containing Semantic Reasoning (SR) examples enriched with cross-page and region-level reasoning layout annotations. Meanwhile, we also introduce an Adaptive Chain-of-Thought (Ada-CoT) strategy, which dynamically adjusts the reasoning process according to different tasks, enabling more efficient and flexible step-wise document understanding. Experiments on several document reasoning benchmarks demonstrate that RegionDoc-R1 achieves state-of-the-art performance across tasks such as form document understanding, table-based QA, and layout-sensitive information extraction.