Multi-Agent Framework Optimizes Long-Horizon Planning with Planner-Centric RL
A recent paper published on arXiv introduces a novel framework for multi-agent collaboration aimed at long-horizon planning through the use of language models. This framework divides automation into three distinct roles: a planner for overarching decision-making, an actor for carrying out tasks, and a memory manager for contextual reasoning. The key contribution of the authors is a comprehensive analysis of compute allocation, revealing that planning significantly impacts task performance, while execution and memory management demand much less computational power and model capacity. Drawing from these findings, they propose a planner-focused reinforcement learning method that optimizes the planner based on trajectory-level rewards from a VLM-as-judge. The paper can be found at arXiv:2605.02168.
Key facts
- arXiv paper 2605.02168 proposes a multi-agent framework for long-horizon planning
- Framework has three roles: planner, actor, memory manager
- Planning is the dominant factor in task performance
- Execution and memory management need less compute
- Planner-centric reinforcement learning optimizes only the planner
- Uses trajectory-level rewards from a VLM-as-judge
- Published on arXiv
- Announce type: new
Entities
Institutions
- arXiv