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Multi-Programming Language Sandbox for LLMs

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arxiv 2410.23074 v2 pith:7O7CNFZZ submitted 2024-10-30 cs.SE cs.CL

Shihan Dou , Jiazheng Zhang , Jianxiang Zang , Yunbo Tao , Weikang Zhou , Haoxiang Jia , Shichun Liu , Yuming Yang
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Zhiheng Xi Shenxi Wu Shaoqing Zhang Muling Wu Changze Lv Limao Xiong Wenyu Zhan Lin Zhang Rongxiang Weng Jingang Wang Xunliang Cai Yueming Wu Ming Wen Rui Zheng Tao Ji Yixin Cao Tao Gui Xipeng Qiu Qi Zhang Xuanjing Huang
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classification cs.SEcs.CL
keywords mplsandboxcodelanguageanalysiscode-relatedllm-basedllmstasks
verification ladder T0 review T1 audit T2 compute T3 formal

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We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox also integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. MPLSandbox can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of their generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we integrate it into training and deployment approaches, and also employ it to optimize workflows for a wide range of real-world code-related tasks. Our goal is to enhance researcher productivity on LLM-based code-related tasks by simplifying and automating workflows through delegation to MPLSandbox.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FullStack Bench: Evaluating LLMs as Full Stack Coders

    cs.AI 2024-11 conditional novelty 6.0 of 10

    FullStack Bench and SandboxFusion offer a broader, execution-based benchmark for code LLMs with 3,374 problems in 16 languages and 11 domains.

  2. Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

    cs.LG 2025-06 conditional novelty 5.0 of 10

    ROLL is a distributed RL training library for LLMs with sample-level rollout scheduling and flexible device mapping, demonstrated on RLVR and agentic tasks but without baseline or scaling measurements.

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