Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent
  Self-Evolution
Cheng Qian Shihao Liang Yujia Qin Yining Ye Xin Cong Yankai Lin Yesai Wu Zhiyuan Liu Maosong Sun
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy
for enhancing the adaptability and flexibility of AI agents through inter-task
self-evolution. Unlike existing methods focused on intra-task learning, ICE
promotes the transfer of knowledge between tasks for genuine self-evolution,
similar to human experience learning. The strategy dynamically investigates
planning and execution trajectories, consolidates them into simplified
workflows and pipelines, and exploits them for improved task execution. Our
experiments on the XAgent framework demonstrate ICE's effectiveness, reducing
API calls by as much as 80% and significantly decreasing the demand for the
model's capability. Specifically, when combined with GPT-3.5, ICE's performance
matches that of raw GPT-4 across various agent tasks. We argue that this
self-evolution approach represents a paradigm shift in agent design,
contributing to a more robust AI community and ecosystem, and moving a step
closer to full autonomy.