Summary of Rl-gpt: Integrating Reinforcement Learning and Code-as-policy, by Shaoteng Liu et al.
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
by Shaoteng Liu, Haoqi Yuan, Minda Hu, Yanwei Li, Yukang Chen, Shu Liu, Zongqing Lu, Jiaya Jia
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A hierarchical framework for Large Language Models (LLMs) is introduced to handle both high-level planning and precise control in embodied tasks. The RL-GPT framework combines a slow agent that analyzes actions suitable for coding with a fast agent that executes coding tasks. This decomposition enables efficient processing, outperforming traditional Reinforcement Learning (RL) methods and existing GPT agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine learning model that can do complex things like play games and make decisions, but also follows rules and does specific tasks well. Scientists have created a new way to organize these models into two parts: one for planning big picture actions and one for doing the detailed work. This helps the model focus on what it needs to do and gets better results than other approaches. |
Keywords
* Artificial intelligence * Gpt * Machine learning * Reinforcement learning
