Summary of The Rl/llm Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models, by Moschoula Pternea et al.
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models
by Moschoula Pternea, Prerna Singh, Abir Chakraborty, Yagna Oruganti, Mirco Milletari, Sayli Bapat, Kebei Jiang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 reviews recent studies combining Reinforcement Learning (RL) and Large Language Models (LLMs). The authors propose a novel taxonomy of three main classes: RL4LLM, LLM4RL, and RL+LLM. RL4LLM involves using RL to improve LLM performance on natural language processing tasks, while LLM4RL uses an LLM to assist RL training for non-linguistic tasks. RL+LLM embeds both models in a common planning framework without training or fine-tuning each other. The taxonomy is used to explore the motivations behind this synergy and identify potential shortcomings, as well as alternative methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how two important areas of computer science – Reinforcement Learning (RL) and Large Language Models (LLMs) – work together. It makes a special list with three main groups: RL helps LLMs get better at language tasks, LLMs help train RL models for other tasks, or both models work together in a special way. The paper explains why these two areas are good friends and what they can learn from each other. |
Keywords
* Artificial intelligence * Fine tuning * Natural language processing * Reinforcement learning
