Summary of Towards Efficient Llm Grounding For Embodied Multi-agent Collaboration, by Yang Zhang et al.
Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration
by Yang Zhang, Shixin Yang, Chenjia Bai, Fei Wu, Xiu Li, Zhen Wang, Xuelong Li
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for multi-agent collaboration in embodied tasks, focusing on grounding the reasoning ability of large language models (LLMs). The challenge lies in planning for coordination among multiple agents without relying excessively on physical verification or self-reflection. To address this issue, the authors introduce Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. Specifically, they employ critic regression to learn a sequential advantage function from LLM-planned data and then optimize the LLM planner using this function. This approach enables the LLM to discern whether an action contributes to achieving the final task. The authors provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experimental results on Overcooked-AI and RoCoBench demonstrate that ReAd surpasses baselines in success rate and significantly reduces interaction steps and query rounds of LLMs, highlighting its efficiency for grounding LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping big language models (LLMs) work better with each other. Right now, it’s hard to teach LLMs how to plan and coordinate with multiple agents in the physical world. The authors propose a new way called Reinforced Advantage feedback (ReAd) that makes planning more efficient. They use this approach to help LLMs learn from their own plans and adjust them to work better together. This improves coordination and reduces the need for the models to constantly ask for confirmation or try out different actions. The results show that ReAd is a successful method that can help LLMs achieve tasks more effectively. |
Keywords
» Artificial intelligence » Grounding » Regression » Reinforcement learning
