ScaleBox提升大模型代码验证的准确性和并发效率。
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
- 自动生成并管理专用评测器，实现精准验证。
- 支持细粒度并行执行，多节点无缝协同，吞吐量显著提升。
- 适合大规模代码训练与可复现基准测试的研究者使用。
代码沙箱已成为推动大语言模型编程能力发展的关键基础设施，为强化学习训练和评估提供可验证反馈。然而，现有系统在高并发负载下难以兼顾验证精度与效率。我们提出ScaleBox，一个高保真且可扩展的系统，用于解决大规模代码训练中的上述局限。ScaleBox引入自动化专用评测器生成与管理机制，支持测试用例的细粒度并行执行及无缝多节点协调，并配备配置驱动的评估套件，实现可复现的基准测试。一系列实验表明，ScaleBox显著提升了代码验证的准确率与效率。进一步的RLVR实验显示，ScaleBox大幅改善了LiveCodeBench上的性能表现与训练稳定性，显著优于基于启发式匹配的基线方法。通过提供可靠且高吞吐的基础设施，ScaleBox促进了大规模代码训练领域的研究与开发。
原文摘要 · Abstract (English)
Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and evaluation. However, existing systems fail to provide accurate verification and efficiency under high-concurrency workloads. We present ScaleBox, a high-fidelity and scalable system designed to address these limitations in large-scale code training. ScaleBox introduces automated special-judge generation and management, fine-grained parallel execution across test cases with seamless multi-node coordination, and a configuration-driven evaluation suite for reproducible benchmarking. A series of experiments demonstrates that ScaleBox significantly enhances code verification accuracy and efficiency. Our further RLVR experiments show that ScaleBox substantially improves both performance on LiveCodeBench and training stability, significantly outperforming heuristic-matching baselines. By providing a reliable and high-throughput infrastructure, ScaleBox facilitates more effective research and development in large-scale code training.
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