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 α-Curriculum Reward, which aligns task difficulty with the agent's current capabilities. We evaluate GenEnv 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, GenEnv achieves better performance while using 3.3times less data. By shifting from static supervision to adaptive simulation, GenEnv provides a data-efficient pathway for scaling agent capabilities.
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Jiacheng Guo
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Ling Yang
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2026-03-12T09:22:19.421Z
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Ling Yang
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Peter Chen
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2025-12-25T20:49:42.770Z
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Peter L. Chen
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Qixin Xiao
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Yinjie Wang
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Xinzhe Juan
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Jiahao Qiu
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Ke Shen
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Mengdi Wang
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 α-Curriculum Reward, which aligns task difficulty with the agent's current capabilities. We evaluate GenEnv 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, GenEnv achieves better performance while using 3.3times less data. By shifting from static supervision to adaptive simulation, GenEnv provides a data-efficient pathway for scaling agent capabilities.
GenEnv, a framework using a co-evolutionary game with a generative environment simulator, enhances LLM agent performance by 40.3% over 7B baselines and uses less data than offline augmentation.
Large Language Model LLM GenEnv co-evolutionary game generative environment simulator dynamic curriculum policy zone of proximal development α-Curriculum Reward API-Bank ALFWorld BFCL Bamboogle TravelPlanner data-efficient adaptive simulation