The field of Large Reasoning Models (LRMs) is moving towards improving safety and robustness in their chain-of-thought reasoning. Recent developments focus on addressing the challenges of harmful content and unsafe reasoning, with a emphasis on explicit alignment methods and dynamic self-correction. Noteworthy papers in this area include: AdvChain, which proposes an adversarial chain-of-thought tuning paradigm to teach models dynamic self-correction, and Towards Safe Reasoning in Large Reasoning Models via Corrective Intervention, which explores process supervision and proposes Intervened Preference Optimization (IPO) to enforce safe reasoning. These innovative approaches demonstrate significant improvements in safety and robustness, and are expected to contribute to the development of more reliable and trustworthy LRMs.