Summary of Empowering Large Language Model Agents Through Action Learning, by Haiteng Zhao et al.
Empowering Large Language Model Agents through Action Learning
by Haiteng Zhao, Chang Ma, Guoyin Wang, Jing Su, Lingpeng Kong, Jingjing Xu, Zhi-Hong Deng, Hongxia Yang
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The proposed LearnAct framework enables Large Language Model (LLM) agents to learn new actions from experience, a crucial aspect of intelligent behavior. Currently, LLM agents operate within fixed action spaces, limiting their potential for growth. The authors introduce an iterative learning strategy that creates and improves Python functions as actions in the form of LLM revisions based on errors identified in unsuccessful training tasks. This approach leads to improved agent performance, with a notable 32% increase in AlfWorld compared to ReAct+Reflexion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLM agents are getting smarter, but they can’t learn from mistakes like humans do. To help them grow, researchers developed a new way for LLMs to create and improve actions based on what works and what doesn’t. This approach helps the agent become better at tasks by learning from its experiences. The results show that this new method makes the agent 32% better at certain types of tasks. |
Keywords
* Artificial intelligence * Large language model
