Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning 事件

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning arXiv:2602.10090v3 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) have empowered autonomous agents to perform multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this p