LLM-in-Sandbox Elicits General Agentic Intelligence
We introduce LLM-in-Sandbox, enabling LLMs to explore within a code sandbox (i.e., a virtual computer), to elicit general intelligence in non-code domains. We first demonstrate that strong LLMs, without additional training, exhibit generalization capabilities to leverage the code sandbox for non-code tasks. For example, LLMs spontaneously access external resources to acquire new knowledge, leverage the file system to handle long contexts, and execute scripts to satisfy formatting requirements. We further show that these agentic capabilities can be enhanced through LLM-in-Sandbox Reinforcement Learning (LLM-in-Sandbox-RL), which uses only non-agentic data to train models for sandbox exploration. Experiments demonstrate that LLM-in-Sandbox, in both training-free and post-trained settings, achieves robust generalization spanning mathematics, physics, chemistry, biomedicine, long-context understanding, and instruction following. Finally, we analyze LLM-in-Sandbox's efficiency from computational and system perspectives, and open-source it as a Python package to facilitate real-world deployment.
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Daixuan Cheng
claimed_verified
2026-01-23T09:38:28.070Z
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Daixuan Cheng
daixuancheng
user
daixuancheng
6972e04dfb12c92b735b73d0
Shaohan Huang
6972e04dfb12c92b735b73d1
Yuxian Gu
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Huatong Song
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Guoxin Chen
6972e04dfb12c92b735b73d4
Li Dong
claimed_verified
2026-01-26T08:32:53.463Z
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Li Dong
unilm
user
unilm
6972e04dfb12c92b735b73d5
Wayne Xin Zhao
6972e04dfb12c92b735b73d6
Ji-Rong Wen
6972e04dfb12c92b735b73d7
Furu Wei
LLM-in-Sandbox Elicits General Agentic Intelligence
We introduce LLM-in-Sandbox, enabling LLMs to explore within a code sandbox (i.e., a virtual computer), to elicit general intelligence in non-code domains. We first demonstrate that strong LLMs, without additional training, exhibit generalization capabilities to leverage the code sandbox for non-code tasks. For example, LLMs spontaneously access external resources to acquire new knowledge, leverage the file system to handle long contexts, and execute scripts to satisfy formatting requirements. We further show that these agentic capabilities can be enhanced through LLM-in-Sandbox Reinforcement Learning (LLM-in-Sandbox-RL), which uses only non-agentic data to train models for sandbox exploration. Experiments demonstrate that LLM-in-Sandbox, in both training-free and post-trained settings, achieves robust generalization spanning mathematics, physics, chemistry, biomedicine, long-context understanding, and instruction following. Finally, we analyze LLM-in-Sandbox's efficiency from computational and system perspectives, and open-source it as a Python package to facilitate real-world deployment.
LLM-in-Sandbox enables large language models to perform general intelligence tasks across diverse domains by allowing them to explore a code sandbox environment, achieving robust generalization without additional training.
LLM-in-Sandbox code sandbox virtual computer reinforcement learning non-agentic data sandbox exploration general intelligence long-context understanding instruction following