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arXiv Machine Learning By Kaixuan Ji, Zixiang Chen, Wenting Zhao, Cheng Qian, Heng Wang, Weizhi Zhang, Jielin Qiu, Liangwei Yang, Quanquan Gu, Silvio Savarese, Shelby Heinecke, Huan Wang

Learning Personalized Memory Gating from Delayed One-Sided Feedback

Read the original on arXiv Machine Learning →

The paper introduces a personalized memory gating approach for language‑model agents, addressing the challenge that a finite memory bank cannot store every fact. It proposes a retrieval‑based labeling strategy to identify which facts are later used, and formulates online memory gating as a contextual bandit problem with delayed one‑sided feedback. The authors present Warm‑Start UCB, combining offline learning with online adaptation, and provide a theoretical regret bound while experimentally showing superior performance over baselines across various memory budgets and history lengths.

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 Machine Learning.

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