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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