Summary of Wese: Weak Exploration to Strong Exploitation For Llm Agents, by Xu Huang et al.
WESE: Weak Exploration to Strong Exploitation for LLM Agents
by Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach called Weak Exploration to Strong Exploitation (WESE) to enhance large language model agents in solving open-world interactive tasks. By decoupling the exploration and exploitation process, WESE employs a cost-effective weak agent for global knowledge acquisition, storing it in a knowledge graph-based strategy. This approach improves success rates and efficiency across four interactive benchmarks, making it flexible enough to incorporate diverse tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, researchers have developed large language models that can learn from complex tasks. However, these models are not very good at exploring new information or exploiting what they already know. When faced with open-world interactive environments, these limitations become apparent. The paper proposes a new approach called WESE to address this issue. It separates the process of exploration and exploitation into two parts: a weak agent that explores and acquires knowledge, and a stronger agent that uses this knowledge to make better decisions. This approach has been tested on four different tasks and shows significant improvements in success rates and efficiency. |
Keywords
» Artificial intelligence » Knowledge graph » Large language model
