The field of large reasoning models is moving towards improving test-time compute performance and tokenization strategies. Recent research focuses on developing innovative methods to enhance model performance, such as dynamically modulating reflection tokens and incorporating domain knowledge into tokenization. These approaches aim to address the limitations of traditional methods and improve the accuracy and efficiency of large reasoning models. Noteworthy papers in this area include those that propose novel tokenization methods and decoding strategies, which have shown significant improvements over existing approaches. Notable papers include: CyclicReflex, which proposes a decoding strategy that dynamically modulates reflection token logits, and Incorporating Domain Knowledge into Materials Tokenization, which integrates material knowledge into tokenization. Learning a Continue-Thinking Token for Enhanced Test-Time Scaling also presents a promising approach, learning a dedicated token to trigger extended reasoning. Additionally, Re-Initialization Token Learning for Tool-Augmented Large Language Models enhances model performance by aligning tool tokens with the existing word embedding space.