ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
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...
| Authors | Jiasheng Zheng, Xin Zheng, Boxi Cao et al. |
|---|---|
| Primary category | cs.SE |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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