Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation

• URL: http://arxiv.org/abs/2609.15721v1
• Date: Mon, 14 Sep 2026 15:20:29 GMT
• Title: Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation
• Abstract summary: We propose CREW (Collaborative Reinforcement Learning for Related Work Generation), a novel framework where LLM agents bypass pipelines to dynamically coordinate by autonomously selecting actions.<n>Our approach yields substantial quality improvements over strong existing baselines, while significantly reducing token costs.
• Score: 11.96857863198177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Related Work Generation (RWG) significantly reduces the human time and effort required to author the Related Work Section (RWS) of a research paper. However, prior methods leveraging multi-agent Large Language Models (LLMs) typically rely on a predefined workflow, where each agent is responsible for a specific step in the entire process. This rigid, static inter-agent coordination limits the adaptive collaboration required to synthesize complex scientific literature. To address this limitation, we propose CREW (Collaborative Reinforcement Learning for Related Work Generation), a novel framework where LLM agents bypass heuristic pipelines to dynamically coordinate by autonomously selecting actions, such as Retrieve, Disseminate, Compose, and Critique, driven by a policy optimized via Independent Proximal Policy Optimization (IPPO). Extensive experiments on a standard RWG benchmark demonstrate that our approach yields substantial quality improvements over strong existing baselines, while significantly reducing token costs. Code is available at https://github.com/YenPBao/CREW-Collaborative-MARL.git

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