Summary of Self-playing Adversarial Language Game Enhances Llm Reasoning, by Pengyu Cheng et al.
Self-playing Adversarial Language Game Enhances LLM Reasoning
by Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Zheng Yuan, Yong Dai, Lei Han, Nan Du, Xiaolong Li
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 abstract explores the potential of self-play training for large language models (LLMs) in an adversarial language game called Adversarial Taboo. The game involves two players, an attacker and a defender, who communicate around a target word only visible to the attacker. The LLMs are trained to play both roles and engage in reinforcement learning on the game outcomes. The results show that the LLMs’ performances uniformly improve on reasoning benchmarks, and that iteratively adopting self-play can continuously promote their reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching large language models to reason better by playing a game with themselves. The game is called Adversarial Taboo and it’s like a puzzle where one player tries to get the other to say a certain word without saying it directly. The model plays both roles and gets better at solving puzzles as it plays more games. This makes it smarter and able to reason better. |
Keywords
» Artificial intelligence » Reinforcement learning
