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

arXiv:2608.06197v2 Announce Type: replace Abstract: 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