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Uni-Mem: Learning a Long-Horizon Agentic Memory Manager with Unified Online Policy Optimization

Zhiqian Xia, Jielong Tang, Ling-An Zeng, Shixiong Kai, Rong Yao, Hao Kong, Xiao Dong, Wei Wang, Mingxuan Yuan, Siyu Xia, Haifeng Xia · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29671 · cited by 0 works (OpenAlex) · open-access PDF

Hindi

लंबी अवधि एजेंट स्मृति स्मृति निर्माण और रिकॉर्डिंग के लिए सीखने योग्य नीतियों के लिए पूर्व-निर्धारित कार्य प्रवाहों से आगे बढ़ रही है। मौजूदा सीखने-आधारित विधियां या तो निर्माण को अकेले अनुकूलित करती हैं या रिकॉर्डिंग-साइड ऑनलाइन मजबूत सीखने (आरएल) को ऑफ़लाइन निर्माण पर्यवेक्षण के साथ जोड़ती हैं, जो दो चरणों के बीच सह-अनुकूलन को सीमित करती है। इस लेख में, हम एक एकीकृत स्मृति प्रबंधक (यूनी-मेम) के लिए अंत से अंत तक ऑनलाइन आरएल का पता लगाते हैं जो एक साझा नीति के तहत दोनों क्षमताओं को सीखता है। हालांकि, मौजूदा आरएल एल्गोरिथ्म

Machine translation (Meta M2M100); the English original follows.

Abstract

Long-term agent memory is shifting from predefined workflows toward learnable policies for memory construction and retrieval. Existing learning-based methods either optimize construction alone or combine retrieval-side online reinforcement learning (RL) with offline construction supervision, limiting co-adaptation between the two stages. In this paper, we explore end-to-end online RL for a unified memory manager (Uni-Mem) that learns both capabilities under a shared policy. However, joint optimization with existing RL algorithms such as GRPO faces confounded group comparisons and sparse feedback over long memory-management trajectories. To this end, we propose Stage-Decoupled GRPO, which alternates construction and retrieval optimization while controlling variation from the other stage within each rollout group, yielding stage-specific learning signals. We further introduce Event-Wise Credit Assignment (ECA), which localizes feedback to responsible events and propagates event-level advantages to their constituent operations. Experiments on LoCoMo and LongMemEval show that Uni-Mem outperforms the strongest RL-based memory, demonstrating the effectiveness of jointly learning memory construction and retrieval via Stage-Decoupled GRPO and ECA.

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Topics: Computer science, Reinforcement learning, Workflow, Artificial intelligence, Limiting, Scheme (mathematics)

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