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arXiv AI By Weiwei Sun, Xuhui Zhou, Weihua Du, Xingyao Wang, Sean Welleck, Graham Neubig, Maarten Sap, Yiming Yang

Training Proactive and Personalized LLM Agents

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The paper proposes shifting AI agent training from isolated task completion to collaborative interaction, defining three key dimensions—Productivity, Proactivity, and Personalization (PPP). It introduces UserVille, an environment with LLM-based user simulators and user-centric feedback, and a multi-objective reinforcement learning framework that optimizes PPP using rewards from task outcomes, question effort, and preference adherence. Experiments on SWE-Bench and BrowseComp-Plus show PPP-trained agents outperform strong LLM baselines, ask more targeted questions, and generalize to unseen preferences and tasks, with a user study underscoring the value of user-centric feedback for effective, supervised collaboration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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