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arXiv · 2507.02259

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

Hongli Yu·Tinghong Chen·Jiangtao Feng·Jiangjie Chen·Weinan Dai·Qiying Yu·Ya-Qin Zhang·Wei-Ying Ma·Jingjing Liu·Mingxuan Wang·Hao Zhou
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Abstract

Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an end-to-end fashion and introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy. We extend the DAPO algorithm to facilitate training via independent-context multi-conversation generation. MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.

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Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou. 2025-07-03. MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent. https://arxiv.org/abs/2507.02259

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