Summary of Pract: Optimizing Principled Reasoning and Acting Of Llm Agent, by Zhiwei Liu et al.
PRACT: Optimizing Principled Reasoning and Acting of LLM Agent
by Zhiwei Liu, Weiran Yao, Jianguo Zhang, Rithesh Murthy, Liangwei Yang, Zuxin Liu, Tian Lan, Ming Zhu, Juntao Tan, Shirley Kokane, Thai Hoang, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Principled Reasoning and Acting (PRAct) framework is a novel method for learning and enforcing action principles from trajectory data. The approach uses text gradients from a reflection and optimization engine to derive these principles. To adapt principles to specific task requirements, the framework proposes Reflective Principle Optimization (RPO), which employs a reflector to critique current principles and an optimizer to update them accordingly. Two RPO methods, Reward-RPO and Self-RPO, are introduced for different settings. Experimental results across four environments demonstrate that the PRAct agent effectively learns and applies action principles to enhance performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PRAct framework is a new way to learn and follow rules from data. It uses special tools to find these rules and then updates them based on how well they work. This helps the framework make better decisions. The results show that this approach works well in different situations. |
Keywords
» Artificial intelligence » Optimization
