GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators

Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenEnv, a framework that establishes a difficulty-aligned co-evolutionary game between an agent and a scalable, generative environment simulator. Unlike traditional methods that evolve models on static datasets, GenEnv instantiates a dataevolving: the simulator acts as a dynamic curriculum policy, continuously generating tasks specifically tailored to the agent's ``zone of proximal development''. This process is guided by a simple but effective -Curriculum Reward, which aligns task difficulty with the agent's current capabilities. We evaluate \autoenv on five benchmarks, including API-Bank, ALFWorld, BFCL, Bamboogle, and TravelPlanner. Across these tasks, GenEnv improves agent performance by up to 40.3% over 7B baselines and matches or exceeds the average performance of larger models. Compared to Gemini 2.5 Pro-based offline data augmentation, \autoenv achieves better performance while using 3.3 less data. By shifting from static supervision to adaptive simulation, \autoenv provides a data-efficient pathway for scaling agent capabilities.