How to train multi-agent AI with limited human feedback?
Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques
This paper investigates how to train multi-agent AI systems using human feedback, specifically focusing on learning from preferences instead of direct rewards. It demonstrates that simply having data from the ideal scenario is not enough; the training data needs to include examples of agents acting independently and sub-optimally. This is particularly relevant for LLM-based multi-agent systems, where aligning multiple LLMs with human preferences and ensuring their cooperative behavior is a major challenge. The paper offers insights into data collection strategies and proposes practical algorithmic techniques like reward regularization and imitation learning to improve the effectiveness of training multi-agent LLM systems with human preferences.