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

• URL: http://arxiv.org/abs/2507.02259v1
• Date: Thu, 03 Jul 2025 03:11:50 GMT
• Title: MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
• Authors: Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou,
• Abstract summary: We introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy.<n>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.
• Score: 53.82053723030023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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