RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems

Wenwen Zeng; Jinhui Zhang; Hao Chen; Zhaoyu Hu; Yongqi Liang; Jiajun Chai; Dengcan Liu; Zhenfeng Liu; Shurui Yan; Minglong Xue; Xiaohan Wang; Wei Lin; Guojun Yin

arXiv · 2605.11874

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems

Wenwen Zeng·Jinhui Zhang·Hao Chen·Zhaoyu Hu·Yongqi Liang·Jiajun Chai·Dengcan Liu·Zhenfeng Liu·Shurui Yan·Minglong Xue·Xiaohan Wang·Wei Lin·Guojun Yin

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Abstract

The integration of Large Language Model (LLM) agents is transforming recommender systems from simple query-item matching towards deeply personalized and interactive recommendations. Reinforcement Learning (RL) provides an essential framework for the optimization of these agents in recommendation tasks. However, current methodologies remain limited by a reliance on single dimensional outcome-based rewards that focus exclusively on final user interactions, overlooking critical intermediate capabilities, such as instruction following and complex intent understanding. Despite the necessity for designing multi-dimensional reward, the field lacks a standardized benchmark to facilitate this development. To bridge this gap, we introduce RecRM-Bench, the largest and most comprehensive benchmark to date for agentic recommender systems. It comprises over 1 million structured entries across four core evaluation dimensions: instruction following, factual consistency, query-item relevance, and fine-grained user behavior prediction. By supporting comprehensive assessment from syntactic compliance to complex intent grounding and preference modeling, RecRM-Bench provides a foundational dataset for training sophisticated reward models. Furthermore, we propose a systematic framework for the construction of multi-dimensional reward models and the integration of a hybrid reward function, establishing a robust foundation for developing reliable and highly capable agentic recommender systems. The complete RecRM-Bench dataset is publicly available at https://huggingface.co/datasets/wwzeng/RecRM-Bench.

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Wenwen Zeng, Jinhui Zhang, Hao Chen, Zhaoyu Hu, Yongqi Liang, Jiajun Chai, Dengcan Liu, Zhenfeng Liu, Shurui Yan, Minglong Xue, Xiaohan Wang, Wei Lin, Guojun Yin. 2026-05-12. RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems. https://arxiv.org/abs/2605.11874

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