Summary of Efficient Reinforcement Learning with Large Language Model Priors, by Xue Yan et al.
Efficient Reinforcement Learning with Large Language Model Priors
by Xue Yan, Yan Song, Xidong Feng, Mengyue Yang, Haifeng Zhang, Haitham Bou Ammar, Jun Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 aim to improve sequential decision-making (SDM) tasks by combining large language models (LLMs) with reinforcement learning (RL). They propose treating LLMs as prior action distributions and integrating them into RL frameworks through Bayesian inference methods. This approach enables the efficient solution of complex SDM tasks while reducing exploration and optimization complexity. The authors demonstrate the effectiveness of their method in offline learning scenarios, showing a significant reduction in required samples compared to traditional RL techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists find a way to make computers better at making decisions over time by combining two powerful tools: large language models and reinforcement learning. They teach computers to use prior knowledge from language models to make decisions more efficiently, reducing the need for trial and error. This new method helps computers solve complex problems faster and with fewer mistakes. |
Keywords
» Artificial intelligence » Bayesian inference » Optimization » Reinforcement learning
