This research paper explores the capabilities of large language models (LLMs) in competitive programming. It compares the performance of OpenAI's o1 and o3 LLMs, highlighting the significant improvement in performance achieved by o3 through increased reinforcement learning. The study also examines a specialized LLM, o1-ioi, fine-tuned for the International Olympiad in Informatics (IOI), demonstrating that scaling general-purpose models surpasses the performance gains from specialized, hand-engineered approaches. Furthermore, the paper evaluates the LLMs' performance on real-world software engineering tasks, showcasing the broad applicability of enhanced reasoning capabilities in coding. Overall, the findings suggest that scaling reinforcement learning in general-purpose LLMs offers a robust path towards achieving state-of-the-art AI in complex reasoning domains.