# 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  
**arXiv:** [2507.02259](https://arxiv.org/abs/2507.02259)  
**Date:** 2025-07-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2507.02259v1/

## Key points

- MemAgent introduced as a novel agent workflow for handling long-context tasks
- Operates by reading text in segments and updating memory using an overwrite strategy
- Extends DAPO algorithm to support training through independent-context multi-conversation generation
- Demonstrates impressive capabilities in handling long-context tasks with minimal performance loss (<5%)
- Achieves over 95% accuracy in the 512K RULER test
- Represents significant advancements in addressing challenges of processing infinitely long documents while maintaining linear complexity

## Summary

The paper titled "MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent" addresses the challenge of efficiently processing long texts without sacrificing performance. The authors - Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang and Hao Zhou - introduce MemAgent as a novel agent workflow designed to handle long-context tasks in an end-to-end manner. MemAgent operates by reading text in segments and updating memory using an overwrite strategy. By extending the DAPO algorithm to support training through independent-context multi-conversation generation, MemAgent showcases impressive capabilities in handling long-context tasks. The model can extrapolate from an 8K context trained on 32K text to a 3.5M question-answering task with minimal performance loss (<5%) and achieves over 95% accuracy in the 512K RULER test. This innovative approach demonstrates significant advancements in addressing the challenges associated with processing infinitely long documents while maintaining linear complexity. The authors' work represents a crucial step forward in enhancing the efficiency and effectiveness of long-text processing algorithms.