Can LLMs solve multi-agent optimization problems faster?
PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
September 9, 2024
https://arxiv.org/pdf/2409.03811This paper introduces PARCO, a new method for efficiently solving multi-agent combinatorial optimization problems like routing and scheduling. PARCO uses a parallel decoding approach, allowing multiple agents to make decisions simultaneously, which speeds up solution construction.
PARCO is particularly relevant to LLM-based multi-agent systems because it:
- Employs a centralized autoregressive policy with a shared action space, allowing it to handle varying numbers of agents. This is similar to how LLMs can be adapted to different tasks and contexts.
- Features a "communication layer" that enables agents to coordinate their actions through message passing, similar to how tokens interact within an LLM. This promotes collaboration and helps find better solutions.
- Demonstrates improved scalability compared to traditional sequential methods, suggesting potential for handling the complexities of large-scale multi-agent applications driven by LLMs.