Summary of Position: Foundation Agents As the Paradigm Shift For Decision Making, by Xiaoqian Liu et al.
Position: Foundation Agents as the Paradigm Shift for Decision Making
by Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, Junge Zhang
First submitted to arxiv on: 27 May 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 In this paper, researchers propose a new approach to decision making using “foundation agents” that combine perception, memory, and reasoning. The goal is to create models that can rapidly adapt to diverse tasks like large language models (LLMs). To achieve this, the authors outline a roadmap for foundation agents, including data collection, self-supervised pretraining, adaptation, and knowledge/value alignment with LLMs. This shift in learning paradigm aims to address challenges in conventional decision making approaches such as low sample efficiency and poor generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision making is important because it helps us make good choices. Right now, computers aren’t very good at this. They can learn quickly from lots of data, but they struggle to apply what they’ve learned to new situations. The researchers in this paper think that if we create special kinds of computer models called “foundation agents,” they could get better at making decisions. These foundation agents would be able to learn from a lot of different tasks and then use that knowledge to make good choices. |
Keywords
» Artificial intelligence » Alignment » Generalization » Pretraining » Self supervised
