Strong LLMs just spontaneously hacked a virtual computer to crush non-code tasks – no fine-tuning needed.
Ever wished your LLM could just live in a sandbox, scripting file ops and external fetches to solve real problems? LLM-in-Sandbox does exactly that: zero-shot, strong models like Llama or GPT explore virtual computers for math, physics, chem, biomed, and more – all via code they generate on the fly.[1]
The framework lets LLMs operate in a code sandbox for non-code domains, using files, scripts, and resources. Boost it with LLM-in-Sandbox-RL on plain data for even wilder generalization. Experiments? Massive wins in long-context and instruction tasks without agent-specific training.[1]
Developers, this is gold for agentic apps: build RL-free agents that plan, act, and learn in dynamic envs. Ties into agentic reasoning surveys – foundational skills like tool use now scale to open-ended worlds, beating chain-of-thought limits.[1]
Vs. frozen prompting (AlphaEvolve) or multi-agent sims, this is lighter: spontaneous sandbox use outperforms, simulating ‘societies of thought’ internally. No heavy infra – just a virtual env.[1]
Grab the framework from the paper, spin up a sandbox (Docker?), test on your dataset. Pair with TTT-Discover for test-time RL. Question: When do we see this in prod agents?
Source: Into AI