Core Idea - The article presents the concept of LLM-in-Sandbox, which allows large language models (LLMs) to explore tasks in a virtual computer environment, significantly enhancing their performance in various non-code domains without additional training [5][40]. Group 1: Technical Advancements - The evolution of large models is being unlocked through different paradigms, including In-Context Learning, Chain-of-Thought, and the recent intelligent agent framework that enables multi-turn interactions and tool usage [2][3]. - LLM-in-Sandbox is proposed as a new paradigm that combines LLMs with a virtual computer, allowing them to autonomously explore and complete tasks, leading to improved performance in fields such as mathematics, physics, chemistry, and long-text understanding [3][7]. Group 2: Design and Implementation - LLM-in-Sandbox features a lightweight, general-purpose design that contrasts with existing software engineering agents that require task-specific environments, thus enhancing generalization and scalability [10][11]. - The environment is based on a Docker Ubuntu setup with minimal pre-installed tools, allowing models to autonomously acquire domain-specific tools as needed [12][13]. Group 3: Experimental Results - Experiments across six non-code domains showed significant performance improvements for LLMs in the LLM-in-Sandbox mode, with enhancements observed in mathematics (+6.6% to +24.2%), physics (+1.0% to +11.1%), and other areas without additional training [20][21]. - The model's ability to autonomously utilize the sandbox environment was demonstrated through case studies, showcasing its capacity for external resource access, file management, and computational execution [21][22][23]. Group 4: Reinforcement Learning Integration - LLM-in-Sandbox RL is introduced to enhance the generalization capabilities of weaker models by training them in the sandbox environment using context-based tasks, which require active exploration [26][29]. - The approach has shown consistent performance improvements across various models, indicating its broad applicability and effectiveness [31]. Group 5: Efficiency and Performance - LLM-in-Sandbox demonstrates cross-domain generalization, achieving consistent performance improvements in multiple downstream tasks, including software engineering [31]. - The deployment of LLM-in-Sandbox can significantly reduce token consumption in long-text scenarios, with reductions of up to 8 times, while maintaining competitive throughput speeds [32][34]. Group 6: Future Prospects - LLM-in-Sandbox transcends traditional text generation capabilities, enabling cross-modal abilities and direct file generation, which could evolve into a universal digital creation system [35][38]. - The article concludes that LLM-in-Sandbox should become the default deployment paradigm for large models, as it offers substantial performance enhancements with minimal deployment costs [40].