Quick insights:
- Strong LLMs spontaneously exploit sandbox tools (file I/O, code execution, external access) to handle long contexts, fetch knowledge, and meet formatting needs without any agent-specific training.
- Reinforcement learning on purely non-agentic data further boosts sandbox exploration, enabling robust generalization across math, physics, chemistry, biomedicine, long-context, and instruction following.
- Delivers major efficiency gains — up to 8× fewer tokens during deployment — making it practical for real-world agentic systems.