💡 The paper introduces the Role-Agent framework, which aims to improve the performance of Large Language Model agents by addressing the limitations of inefficient interaction feedback and static training environments. The problem with current Large Language Model agents is that their learning is hindered by the lack of effective feedback and the inability to adapt to changing environments, resulting in limited generalization.

To address this issue, the Role-Agent framework enables a single Large Language Model to function as both the agent and the environment, allowing for a bootstrapped co-evolution process. This framework consists of two components: World-In-Agent and Agent-In-World. The World-In-Agent component uses the Large Language Model as the agent to predict future states after each action, and the alignment between predicted and actual states is used as a reward to encourage environment-aware reasoning.

The Agent-In-World component analyzes failure modes from failed trajectories and retrieves tasks with similar failure patterns, thereby reshaping the training data distribution for targeted practice. This allows the Large Language Model to focus on improving its performance in areas where it is struggling.

The results of the experiments show that the Role-Agent framework consistently improves performance, with an average gain of over 4 percent over strong baselines. This demonstrates the effectiveness of the Role-Agent framework in improving the performance of Large Language Model agents by enabling them to adapt to changing environments and focus on targeted practice. Overall, the Role-Agent framework provides a novel approach to improving the performance of Large Language Model agents, and its results have significant implications for the development of more effective and adaptive language models.