Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation
CREW (Collaborative Reinforcement Learning for Related Work Generation) is proposed, a novel framework where LLM agents bypass heuristic pipelines to dynamically coordinate by autonomously selecting actions, driven by a policy optimized via Independent Proximal Policy Optimization (IPPO).
Hai-Dang Dang, B. Phạm, B. Nguyen et al.
· 0 citations