Summary of Large Language Models Are In-context Preference Learners, by Chao Yu et al.
Large Language Models are In-context Preference Learners
by Chao Yu, Qixin Tan, Hong Lu, Jiaxuan Gao, Xinting Yang, Yu Wang, Yi Wu, Eugene Vinitsky
First submitted to arxiv on: 22 Oct 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 This paper presents In-Context Preference Learning (ICPL), a novel approach to preference-based reinforcement learning that leverages the capabilities of Large Language Models (LLMs). By utilizing LLMs’ in-context learning capabilities, ICPL reduces human query inefficiency and achieves sample-efficient preference learning. The authors demonstrate the effectiveness of ICPL through a synthetic preference study, showing that it outperforms baseline methods with higher performance and orders of magnitude greater efficiency. Additionally, they perform real human preference-learning trials, observing that ICPL extends beyond synthetic settings and can work effectively with humans-in-the-loop. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Preference-based reinforcement learning helps us solve tasks where rewards are hard to specify. However, it’s often slow because we have to teach computers what is good or bad by giving them lots of feedback. This paper shows that Large Language Models (LLMs) are better at learning preferences than we thought! They can learn what we like and dislike just from looking at how well they do a task. The authors create a new method called In-Context Preference Learning (ICPL), which uses LLMs to make it easier for us to teach them what we want. ICPL works by showing the computer videos of its actions and asking us if those actions are good or bad. This helps the computer learn faster and better. |
Keywords
* Artificial intelligence * Reinforcement learning
