Summary of How Can Llm Guide Rl? a Value-based Approach, by Shenao Zhang et al.
How Can LLM Guide RL? A Value-Based Approach
by Shenao Zhang, Sirui Zheng, Shuqi Ke, Zhihan Liu, Wanxin Jin, Jianbo Yuan, Yingxiang Yang, Hongxia Yang, Zhaoran Wang
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 explores the intersection of reinforcement learning (RL) and large language models (LLMs). While LLMs excel in language understanding and generation, they struggle with exploration and self-improvement. The authors develop an algorithm called LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, reducing the amount of data needed for learning. This approach is particularly effective when the initial policy is close to optimal. Additionally, the paper presents SLINVIT, a practical algorithm that simplifies the construction of the value function and employs subgoals to reduce search complexity. Experiments across three interactive environments demonstrate state-of-the-art success rates and sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper brings together two powerful tools: reinforcement learning (RL) and large language models (LLMs). LLMs are great at understanding and generating language, but they struggle to learn and improve on their own. The authors create a new algorithm that helps RL work better with LLMs. This makes it possible for machines to learn from feedback and make good decisions without needing as much data or trial-and-error. The results show that this approach works really well in different environments. |
Keywords
* Artificial intelligence * Language understanding * Regularization * Reinforcement learning
