@inproceedings{li-etal-2025-test,
    title = "{S}*: Test Time Scaling for Code Generation",
    author = "Li, Dacheng  and
      Cao, Shiyi  and
      Cao, Chengkun  and
      Li, Xiuyu  and
      Tan, Shangyin  and
      Keutzer, Kurt  and
      Xing, Jiarong  and
      Gonzalez, Joseph E.  and
      Stoica, Ion",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.865/",
    doi = "10.18653/v1/2025.findings-emnlp.865",
    pages = "15964--15978",
    ISBN = "979-8-89176-335-7",
    abstract = "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* augments the existing parallel scaling approach with sequential scaling to further increase the performance. 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 S* across 12 Large Language Models and Large Reasoning Models and show that: (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{\%}. Codes, model generations and intermediate experiments results are available under Codes, model generations and intermediate ex-periments results are available under https://github.com/NovaSky-AI/SkyThought."
}