The field of large language models is witnessing a significant shift towards test-time scaling, which enables the enhancement of reasoning capabilities without relying on larger models. This approach has shown promising results in automating program improvements, coding tasks, and complex problem-solving. The community is moving towards developing more efficient and effective methods for test-time scaling, including the use of code-related reasoning trajectories, progressive training, and data distillation. Notable papers in this area include 'Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute', which proposes a unified framework for test-time compute scaling, and 'Z1: Efficient Test-time Scaling with Code', which presents an efficient method for reducing excess thinking tokens while maintaining performance. 'OpenCodeReasoning: Advancing Data Distillation for Competitive Coding' also stands out for its state-of-the-art coding capability results achieved through supervised fine-tuning.