AI RESEARCH
FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learning
arXiv CS.LG
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ArXi:2603.11901v1 Announce Type: new Modern recommender systems must adapt to dynamic, need-specific objectives for diverse recommendation scenarios, yet most traditional recommenders are optimized for a single static target and struggle to reconfigure behavior on demand. Recent advances in reinforcement-learning-based post-