Summary of Llms Are In-context Bandit Reinforcement Learners, by Giovanni Monea et al.
LLMs Are In-Context Bandit Reinforcement Learners
by Giovanni Monea, Antoine Bosselut, Kianté Brantley, Yoav Artzi
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper explores a contextual bandit version of in-context reinforcement learning (ICRL), which enables Large Language Models (LLMs) to learn from external rewards rather than supervised data. The study demonstrates that LLMs effectively learn through this process, and provides a detailed investigation of the phenomena, experimenting with various classification tasks and models ranging from 500M to 70B parameters. The research highlights the capabilities of ICRL in LLMs, while also identifying fundamental limitations in their implicit reasoning about errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models can learn new things by getting rewards or punishments for making correct or incorrect decisions. Instead of being taught by a teacher, these models figure out what to do on their own based on the feedback they receive. The researchers tried this approach with different types of tasks and sizes of models, and found that it works well. They also discovered some limitations in how these models think about mistakes. |
Keywords
» Artificial intelligence » Classification » Reinforcement learning » Supervised
