38 lines
1.4 KiB
Markdown
38 lines
1.4 KiB
Markdown
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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---
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## 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)
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* **Blog:** [https://memagent-sialab.github.io/](https://memagent-sialab.github.io/)
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* **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:
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```bibtex
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@article{yu2025memagent,
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title={MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent},
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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},
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journal={arXiv preprint arXiv:2507.02259},
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year={2025}
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}
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```
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