This episode discusses CORY, a new method for fine-tuning large language models (LLMs) using a cooperative multi-agent reinforcement learning framework. Instead of relying on a single agent, CORY utilizes two LLM agents—a pioneer and an observer—that collaborate to improve their performance. The pioneer generates responses independently, while the observer generates responses based on both the query and the pioneer’s response. The agents alternate roles during training to ensure mutual learning and benefit from coevolution. The episode covers CORY's advantages over traditional methods like PPO, including better policy optimality, resistance to distribution collapse, and more stable training. CORY was tested on sentiment analysis and math reasoning tasks, showing superior performance.
The discussion also highlights CORY's potential impact on improving LLMs for specialized tasks, while acknowledging potential risks of misuse.
https://arxiv.org/pdf/2410.06101