Summary of Reasoning, Memorization, and Fine-tuning Language Models For Non-cooperative Games, by Yunhao Yang et al.
Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games
by Yunhao Yang, Leonard Berthellemy, Ufuk Topcu
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 We present a novel approach that combines tree of thoughts and multi-agent framework to enhance the capabilities of pre-trained language models in tackling complex, unfamiliar games. Our method breaks down game-solving into four incremental tasks: summarization, area selection, action extraction, and validation. Each task is assigned to a specific language-model agent, which collaboratively distills game representations and tactics through reasoning paths simulated by the tree of thoughts. The proposed approach also incorporates an automated fine-tuning process that optimizes agents’ performance based on game outcomes. We demonstrate the effectiveness of our method in a non-cooperative game, achieving a 65% winning rate against benchmark algorithms, with an additional 10% improvement after fine-tuning. In contrast to existing deep learning algorithms, our approach requires only approximately 1000 training samples, highlighting its efficiency and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re developing a new way for computers to learn how to play complex games by working together as teams. Our team uses a special system that helps them figure out the best moves by breaking down the game into smaller tasks. This allows each computer to specialize in one area, like summarizing the game or choosing the next move. We tested our approach and found that it can beat other algorithms at playing games, with only 1000 examples of how to play! This is a big improvement over existing methods that need millions of examples. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Language model » Summarization
