LLM-in-Sandbox 提出了一个简洁而有效的范式：通过给大模型提供一台虚拟电脑，让其自由探索来完成任务。实验表明，这一范式能够显著提升模型在非代码领域的表现，且无需额外训练。 研究者认为， LLM-in-Sandbox 应当成为大模型的默认部署范式 ， 取代纯 LLM 推理 。当沙盒可以带来显著的性能提升，并且部署成本几乎可以忽略不计时，为什么还要用纯 LLM？

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这就好比我们突然发现，原本只会纸上谈兵的军师，一旦给了他一套完整的作战工具，立刻就能指挥千军万马。研究结果显示，不同的AI模型在获得沙盒环境后，性能提升幅度从1%到24%不等，这在AI领域算是相当可观的进步。

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