RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866909035581669376 |
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| author | Zeng, Wenwen Zhang, Jinhui Chen, Hao Hu, Zhaoyu Liang, Yongqi Chai, Jiajun Liu, Dengcan Liu, Zhenfeng Yan, Shurui Xue, Minglong Wang, Xiaohan Lin, Wei Yin, Guojun |
| author_facet | Zeng, Wenwen Zhang, Jinhui Chen, Hao Hu, Zhaoyu Liang, Yongqi Chai, Jiajun Liu, Dengcan Liu, Zhenfeng Yan, Shurui Xue, Minglong Wang, Xiaohan Lin, Wei Yin, Guojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11874 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems Zeng, Wenwen Zhang, Jinhui Chen, Hao Hu, Zhaoyu Liang, Yongqi Chai, Jiajun Liu, Dengcan Liu, Zhenfeng Yan, Shurui Xue, Minglong Wang, Xiaohan Lin, Wei Yin, Guojun Information Retrieval 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. |
| title | RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2605.11874 |