Reproduce “RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems” on OpenResearch

About this paper

Wenwen Zeng, Jinhui Zhang, Hao Chen, Zhaoyu Hu, Yongqi Liang, Jiajun Chai, Dengcan Liu, Zhenfeng Liu and 5 more

Researchers from Meituan and affiliated universities developed RecRM-Bench, a comprehensive benchmark, and RecRM-RL, a reinforcement learning framework, to enable multidimensional reward modeling for agentic recommender systems. This approach integrates instruction following, factual consistency, query-item relevance, and user behavior prediction, accelerating training convergence and enhancing behavioral prediction accuracy.

Key insights

• Single-dimensional, outcome-based rewards are insufficient for optimizing agentic recommender systems due to reward sparsity and credit assignment problems in multi-step decision processes.
• Explicit, process-level rewards for instruction following, factual consistency, and query-item relevance are crucial for guiding agents through intermediate steps, stabilizing training, and improving learning efficiency.
• A hierarchical integration of multi-dimensional reward signals significantly accelerates training convergence and improves final behavioral prediction by acting as search-space optimizers and pruning ineffective agent paths.

Summary by alphaXiv.