Training agents with internal world rehearsal—where the agent simulates environment responses—is more efficient than external environment interaction and enables better generalization across tool-use tasks.
EnvACE trains AI agents to use tools by having them internally simulate environment responses instead of relying on external simulators. The agent learns to both act and play the environment's role, building an internal world model that predicts what happens after each action. This approach reduces the need for expensive external environments while improving performance on tool-use benchmarks.