How can I specialize LLMs in a multi-agent system efficiently?
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
This paper introduces LoRASA, a new method for training multi-agent AI systems that allows individual agents to specialize while still sharing a common policy foundation. This is achieved by adding small, low-rank adaptation matrices to the shared policy, enabling efficient specialization without the overhead of training completely separate policies for each agent.
For LLM-based multi-agent systems, LoRASA offers a scalable way to train specialized LLMs for different roles within a multi-agent application. By sharing a core LLM and fine-tuning with low-rank adaptations, it reduces computational and memory costs compared to training fully independent LLMs, while enabling agents to develop diverse behaviors and expertise. This is especially relevant for complex applications with many agents, where training individual LLMs would be resource-intensive. The paper also suggests that fine-tuning all layers of the LLM with LoRASA adapters, rather than just the output layers, offers more robust specialization. The concept of "rank" in LoRASA provides a tunable knob to balance specialization and resource efficiency.