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

Authors

Jiacheng Guo; Ling Yang; Peter Chen; Qixin Xiao; Yinjie Wang; Xinzhe Juan; Jiahao Qiu; Ke Shen; Mengdi Wang

Scores

Rationale

GenEnv presents a novel approach by introducing a co-evolutionary framework for training LLM agents through dynamic environment simulators, addressing data efficiency and adaptability issues in agent training. The technical significance is high as it tackles major bottlenecks like data efficiency and dynamic curriculum learning. The framework's concept of evolving environments can potentially transfer to various domains beyond NLP. It aligns well with current research trends focusing on adaptive learning and efficient data usage. The empirical evidence is strong, demonstrated by significant performance improvements across multiple benchmarks, though more details on the experimental setup would strengthen it further. The potential for influencing future research is substantial, given its innovative approach to agent training and environment co-evolution.