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Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning by Lin, Jiacheng; Wang, Tian; Qian, Kun is a scholarly article available to read on EtoBox.

What is Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning about?

We propose Rec-R1, a general reinforcement learning framework that bridges large language models (LLMs) with recommendation systems through closed-loop optimization. Unlike prompting and supervised fine-tuning (SFT), Rec-R1 directly optimizes LLM generation using feedback from a fixed black-box recommendation model, without relying on synthetic SFT data from proprietary models such as GPT-4o. This avoids the substantial cost and effort required for data distillation. To verify the effectiveness of Rec-R1, we evaluate it on two representative tasks: product search and sequential recommendation. Experimental results demonstrate that Rec-R1 not only consistently outperforms prompting- and SFT-based methods, but also achieves significant gains over strong discriminative baselines, even when used with simple retrievers such as BM25. Moreover, Rec-R1 preserves the general-purpose capabilities of the LLM, unlike SFT, which often impairs instruction-following and reasoning. These findings suggest Rec-R1 as a promising foundation for continual task-specific adaptation without catastrophic forgetting.

Author

Lin, Jiacheng; Wang, Tian; Qian, Kun

Published

2025

Language

EN