The field of Visual Question Answering (VQA) is moving towards more explicit and interpretable reasoning processes. Recent developments have focused on integrating chain-of-thought (CoT) reasoning into VQA frameworks, enabling models to generate intermediate rationales and improve their performance on complex tasks. This shift towards more transparent and structured reasoning has led to significant gains in accuracy and robustness, particularly in high-stakes domains such as climate monitoring and medical decision-making. Noteworthy papers in this area include Hindsight Distillation Reasoning with Knowledge Encouragement Preference, which proposes a framework for eliciting and harnessing internal knowledge reasoning ability in multimodal large language models, and Geospatial Chain of Thought Reasoning, which integrates CoT reasoning with Direct Preference Optimization to improve interpretability and accuracy in satellite imagery analysis. Additionally, CoTBox-TTT and Curriculum-based Relative Policy Optimization have shown promising results in adapting VQA models to new domains and improving their performance on visual grounding tasks. Uncertainty-Guided Lookback has also been proposed as a decoding strategy that combines uncertainty signals with adaptive lookback prompts to improve visual reasoning performance.