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[2602.16173] Learning Personalized Agents from Human Feedback
Summary
The paper presents a framework, Personalized Agents from Human Feedback (PAHF), which enables AI agents to adapt to individual user preferences through continual learning from live interactions.
Why It Matters
As AI agents become increasingly integrated into daily life, their ability to adapt to the unique and evolving preferences of users is crucial. This research addresses the limitations of existing models that rely on static datasets, offering a solution that enhances user experience and personalization.
Key Takeaways
- PAHF allows AI agents to learn from live interactions, improving personalization.
- The framework includes a three-step loop for preference clarification, action grounding, and feedback integration.
- Empirical results show PAHF outperforms traditional models in personalization accuracy and adaptability.
Computer Science > Artificial Intelligence arXiv:2602.16173 (cs) [Submitted on 18 Feb 2026] Title:Learning Personalized Agents from Human Feedback Authors:Kaiqu Liang, Julia Kruk, Shengyi Qian, Xianjun Yang, Shengjie Bi, Yuanshun Yao, Shaoliang Nie, Mingyang Zhang, Lijuan Liu, Jaime Fernández Fisac, Shuyan Zhou, Saghar Hosseini View a PDF of the paper titled Learning Personalized Agents from Human Feedback, by Kaiqu Liang and 11 other authors View PDF HTML (experimental) Abstract:Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either training implicit preference models on interaction history or encoding user profiles in external memory. However, these approaches struggle with new users and with preferences that change over time. We introduce Personalized Agents from Human Feedback (PAHF), a framework for continual personalization in which agents learn online from live interaction using explicit per-user memory. PAHF operationalizes a three-step loop: (1) seeking pre-action clarification to resolve ambiguity, (2) grounding actions in preferences retrieved from memory, and (3) integrating post-action feedback to update memory when preferences drift. To evaluate this capability, we develop a four-phase protocol and two benchmarks in embodied manipulation and online shopping. These benchmarks quantify an agent's ability to learn initial preferences from s...
