MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
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Computer Science > Computation and Language
Title:MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
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.
| Comments: | Accepted to ICLR 2026 as an Oral presentation. OpenReview: this https URL Project page: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2507.02259 [cs.CL] |
| (or arXiv:2507.02259v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2507.02259
arXiv-issued DOI via DataCite
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| Journal reference: | International Conference on Learning Representations (ICLR), 2026 |
Submission history
From: Hongli Yu [view email][v1] Thu, 3 Jul 2025 03:11:50 UTC (4,735 KB)
[v2] Wed, 29 Jul 2026 12:55:39 UTC (4,728 KB)
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