A Goal Without a Plan Is Just a Wish: Efficient and Effective Global
  Planner Training for Long-Horizon Agent Tasks
Authors: name: Shuzheng Si, name: Haozhe Zhao, name: Kangyang Luo, name: Gang Chen, name: Fanchao Qi, name: Minjia Zhang, name: Baobao Chang, name: Maosong Sun

Agents based on large language models (LLMs) struggle with brainless
trial-and-error and generating hallucinatory actions due to a lack of global
planning in long-horizon tasks. In this paper, we introduce a plan-and-execute
framework and propose EAGLET, an efficient and effective planner training
method to enhance the executor agent's planning abilities without human effort.
Specifically, we train a plug-and-play global planner through a two-step
process: we first synthesize high-quality plans from an advanced LLM using our
proposed homologous consensus filtering strategy, and apply fine-tuning as a
cold start. Moreover, we further improve the planner with a rule-based
reinforcement learning stage using a novel executor capability gain reward,
ensuring it can handle task instructions of varying difficulty. Experiments on
three long-horizon agent tasks show that executor agents equipped with our
planner outperform existing methods, achieving new state-of-the-art
performance. Meanwhile, EAGLET reduces training costs by 8x compared to
RL-based baselines, and it does not require manual effort or extra training
data, offering an efficient and effective solution.