Summary of Llms For Generalizable Language-conditioned Policy Learning Under Minimal Data Requirements, by Thomas Pouplin et al.
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements
by Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun, Mihaela van der Schaar
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper introduces TEDUO, a novel approach for training autonomous agents to execute complex decision-making tasks specified by humans in natural language. The proposed method, offline language-conditioned policy learning, leverages large language models (LLMs) to enhance the fidelity of pre-collected data and enable flexible generalization to new goals and states. This is achieved by using LLMs as both data enhancers and generalizers. The authors demonstrate the effectiveness and efficiency of their approach through empirical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to make good decisions without needing lots of training or examples. Right now, computers need a lot of information to learn how to make decisions on their own. This paper shows how to use special computer programs called large language models (LLMs) to help the computer learn and make better decisions even when it’s faced with new situations. |
Keywords
» Artificial intelligence » Generalization
