EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult to ground. We introduce EnvACE, an agentic reinforcement learning method that replaces external environment interaction during training with world rehearsal. The policy alternates between acting and rehearsal: it first generates a tool call, then plays the role

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ethics.ai (6 August 2026), “EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning,” evidence record 17340, https://ethics.ai/record/17340 (originally published by arXiv cs.AI).

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