Planner Matters! An Efficient and Unbalanced Multi-agent Collaboration Framework for Long-horizon Planning

TL;DR

This paper presents a multi-agent framework for long-horizon planning that emphasizes the importance of high-level planning, demonstrating that optimizing the planner alone significantly improves performance and efficiency.

Contribution

The paper introduces a systematic compute-allocation analysis and a planner-centric reinforcement learning approach, highlighting planning as the key factor in long-horizon task success.

Findings

Planning dominates task performance over execution and memory.

Focusing on the planner improves robustness and efficiency.

Experiments show significant gains across diverse benchmarks.

Abstract

Language model (LM)-based agents have demonstrated promising capabilities in automating complex tasks from natural language instructions, yet they continue to struggle with long-horizon planning and reasoning. To address this, we propose an enhanced multi-agent framework that decomposes automation into three roles: a planner for high-level decision-making, an actor for task execution, and a memory manager for contextual reasoning. While this modular decomposition aligns with established design patterns, our core contribution lies in a systematic compute-allocation analysis, revealing that planning is the dominant factor influencing task performance. Execution and memory management require significantly less compute and model capacity to achieve competitive results. Building on these insights, we introduce a planner-centric reinforcement learning approach, which exclusively optimizes the planner using trajectory-level rewards from a VLM-as-judge, while freezing the other components. Extensive experiments on benchmarks spanning web navigation, OS control, and tool use demonstrate that concentrating model capacity and learning on high-level planning yields robust and compute-efficient improvements in long-horizon agent automation. Our code is publicly released.