Summary of Reinforcement Learning Enhanced Llms: a Survey, by Shuhe Wang et al.
Reinforcement Learning Enhanced LLMs: A Survey
by Shuhe Wang, Shengyu Zhang, Jie Zhang, Runyi Hu, Xiaoya Li, Tianwei Zhang, Jiwei Li, Fei Wu, Guoyin Wang, Eduard Hovy
First submitted to arxiv on: 5 Dec 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 the intersection of reinforcement learning (RL) and large language models (LLMs), specifically highlighting the performance gains achieved by models like DeepSeek-R1. However, the complexity of implementing RL-enhanced LLMs hinders research progress, requiring advanced algorithms, reward modeling strategies, and optimization techniques. The study aims to bridge this knowledge gap by providing a comprehensive survey of existing research on RL-enhanced LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence (AI) uses rewards to improve language models. Researchers are excited about the results, but it’s hard for them to understand how these improvements work because it takes special algorithms and strategies. To help with this problem, the study wants to collect and summarize what we know so far about using rewards to improve language models. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
