Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911148825116672 |
|---|---|
| author | Fu, Jia Yang, Xinyu Zhang, Hongzhi Liu, Yahui Zhang, Jingyuan Wang, Qi Zhang, Fuzheng Zhou, Guorui |
| author_facet | Fu, Jia Yang, Xinyu Zhang, Hongzhi Liu, Yahui Zhang, Jingyuan Wang, Qi Zhang, Fuzheng Zhou, Guorui |
| contents | Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a profoundly challenging and unsolved problem. In this work, we present Klear-CodeTest, a comprehensive test case synthesis framework featuring rigorous verification to ensure quality and reliability of test cases. Our approach achieves broad coverage of programming problems via a novel Generator-Validation (G-V) framework, ensuring correctness through a consistency validation mechanism that verifies outputs against gold solutions. The proposed G-V framework generates comprehensive test cases including both regular and corner cases, enhancing test coverage and discriminative power for solution correctness assessment in code reinforcement learning. In addition, we design a multi-layered security sandbox system optimized for online verification platforms, guaranteeing safe and reliable code execution. Through comprehensive experiments, we demonstrate the effectiveness of our curated dataset, showing significant improvements in model performance and training stability. The source codes, curated dataset and sandbox system are available at: https://github.com/Kwai-Klear/CodeTest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning Fu, Jia Yang, Xinyu Zhang, Hongzhi Liu, Yahui Zhang, Jingyuan Wang, Qi Zhang, Fuzheng Zhou, Guorui Software Engineering Artificial Intelligence Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a profoundly challenging and unsolved problem. In this work, we present Klear-CodeTest, a comprehensive test case synthesis framework featuring rigorous verification to ensure quality and reliability of test cases. Our approach achieves broad coverage of programming problems via a novel Generator-Validation (G-V) framework, ensuring correctness through a consistency validation mechanism that verifies outputs against gold solutions. The proposed G-V framework generates comprehensive test cases including both regular and corner cases, enhancing test coverage and discriminative power for solution correctness assessment in code reinforcement learning. In addition, we design a multi-layered security sandbox system optimized for online verification platforms, guaranteeing safe and reliable code execution. Through comprehensive experiments, we demonstrate the effectiveness of our curated dataset, showing significant improvements in model performance and training stability. The source codes, curated dataset and sandbox system are available at: https://github.com/Kwai-Klear/CodeTest. |
| title | Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05710 |