Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the first hybrid test-time scaling framework that substantially improves the coverage and selection accuracy of generated code. S* extends the existing parallel scaling paradigm with sequential scaling to push performance boundaries. It further leverages a novel selection mechanism that adaptively generates distinguishing inputs for pairwise comparison, combined with execution-grounded information to robustly identify correct solutions. We evaluate across 12 Large Language Models and Large Reasoning Model and show: (1) S* consistently improves performance across model families and sizes, enabling a 3B model to outperform GPT-4o-mini; (2) S* enables non-reasoning models to surpass reasoning models - GPT-4o-mini with S* outperforms o1-preview by 3.7% on LiveCodeBench; (3) S* further boosts state-of-the-art reasoning models - DeepSeek-R1-Distill-Qwen-32B with S* achieves 85.7% on LiveCodeBench, approaching o1 (high) at 88.5%. Code will be available under https://github.com/NovaSky-AI/SkyThought.

本研究针对大型语言模型在代码生成领域测试时间计算不足的问题，提出了首个混合测试时间缩放框架S*，显著提升了生成代码的覆盖率和选择准确性。S*结合了顺序缩放和创新选择机制，能够自适应生成区分输入，提高了正确解决方案的识别能力。实验表明，S*在不同模型家族和规模中均表现出一致的性能提升，推动了非推理模型的表现超越推理模型。 

S*: 测试时间缩放在代码生成中的应用