| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen2.5-7B-Instruct |
| --- |
| |
| ## Model Description |
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| The **RL-MemAgent-7B** is a part of the **MemAgent** framework, which enables Large Language Models (LLMs) to process arbitrarily long texts through end-to-end Reinforcement Learning without altering their core architecture. |
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| ## Usage |
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| This model is ideal for tasks requiring the understanding and processing of very long documents, such as comprehensive question answering, summarizing extensive reports, or analyzing large codebases. |
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| For detailed instructions on how to use, evaluate, and train models within the MemAgent framework, please refer to the main [MemAgent GitHub repository](https://github.com/BytedTsinghua-SIA/MemAgent). |
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| ## Links |
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| * **Paper:** [https://arxiv.org/abs/2507.02259](https://arxiv.org/abs/2507.02259) |
| * **Blog:** [https://memagent-sialab.github.io/](https://memagent-sialab.github.io/) |
| * **GitHub:** [https://github.com/BytedTsinghua-SIA/MemAgent](https://github.com/BytedTsinghua-SIA/MemAgent) |
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| ## Citation |
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| If you find this work useful, please consider citing our paper: |
|
|
| ```bibtex |
| @article{yu2025memagent, |
| title={MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent}, |
| author={Yu, Hongli and Chen, Tinghong and Feng, Jiangtao and Chen, Jiangjie and Dai, Weinan and Yu, Qiying and Zhang, Ya-Qin and Ma, Wei-Ying and Liu, Jingjing and Wang, Mingxuan and others}, |
| journal={arXiv preprint arXiv:2507.02259}, |
| year={2025} |
| } |
| ``` |
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|
