Summary of Coevolving with the Other You: Fine-tuning Llm with Sequential Cooperative Multi-agent Reinforcement Learning, by Hao Ma et al.
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
by Hao Ma, Tianyi Hu, Zhiqiang Pu, Boyin Liu, Xiaolin Ai, Yanyan Liang, Min Chen
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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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 In this paper, the authors propose CORY, a novel reinforcement learning (RL) fine-tuning method for large language models (LLMs). CORY extends RL to a sequential cooperative multi-agent framework, leveraging coevolution and emergent capabilities. The LLM is duplicated into two autonomous agents: a pioneer and an observer. They train together, exchanging roles periodically to foster cooperation. Experiments evaluate CORY’s performance on GPT-2 and Llama-2 using IMDB Review and GSM8K datasets respectively. Results show CORY outperforms PPO in policy optimality, resistance to distribution collapse, and training robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CORY is a new way to improve language models like GPT-2 and Llama-2. It uses teamwork between two agents to make decisions. This helps the model learn better and be more robust. The authors tested CORY on some datasets and it did much better than the usual PPO method. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Llama » Reinforcement learning
