@inproceedings{zheng-etal-2026-scalebox,
title = "{S}cale{B}ox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models",
author = "Zheng, Jiasheng and
Zheng, Xin and
Cao, Boxi and
Wang, Pengbo and
Ma, Zhengzhao and
Zhu, Qiming and
Jiang, Jiazhen and
Lu, Yaojie and
Lin, Hongyu and
Han, Xianpei and
Sun, Le",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.30/",
pages = "307--317",
ISBN = "979-8-89176-392-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
%A Zheng, Jiasheng
%A Zheng, Xin
%A Cao, Boxi
%A Wang, Pengbo
%A Ma, Zhengzhao
%A Zhu, Qiming
%A Jiang, Jiazhen
%A Lu, Yaojie
%A Lin, Hongyu
%A Han, Xianpei
%A Sun, Le
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F zheng-etal-2026-scalebox
%X 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.
%U https://aclanthology.org/2026.acl-demo.30/
%P 307-317
Markdown (Informal)
[ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models](https://aclanthology.org/2026.acl-demo.30/) (Zheng et al., ACL 2026)
ACL
- Jiasheng Zheng, Xin Zheng, Boxi Cao, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, and Le Sun. 2026. ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 307–317, San Diego, California, United States. Association for Computational Linguistics.