初始化项目，由ModelHub XC社区提供模型

Model: KDEGroup/SWE-AGILE-RL-8B
Source: Original Platform

README.md

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+---
+tags:
+- agent
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# SWE-AGILE
+
+## 📣 News
+
+[2026/02/23] SWE-AGILE has been accepted to the ACL 2026 Findings.
+
+<font size=4><div align='center' > [[📖 Paper](https://huggingface.co/papers/2604.11716)] [[🤗 Checkpoints](https://huggingface.co/KDEGroup)] [[🤗 Daily Paper](https://huggingface.co/papers/2604.11716)] [[🚀 Github](https://github.com/KDEGroup/SWE-AGILE)]</div></font>
+
+## 🔥 Overview
+
+Prior approaches typically lack the **explicit System-2 reasoning** required for deep analysis. While recent reasoning models demonstrate the potential of extended Chain-of-Thought (CoT), applying them to multi-turn tasks creates a dilemma: retaining full history leads to **context explosion**, while discarding it causes **redundant re-reasoning**.
+
+We propose SWE-AGILE, a novel software agent framework designed to bridge the gap between reasoning depth, efficiency, and context constraints. SWE-AGILE introduces a Dynamic Reasoning Context strategy, maintaining a “sliding window” of detailed reasoning for immediate continuity to prevent redundant re-analyzing, while compressing historical reasoning content into concise Reasoning Digests via **Backfilling Data Synthesis**, **Trajectory Snapshot Training** and **Compression-Aware Optimization**.
+
+
+
+While our current paradigm implicitly reduces redundant state reconstruction, a highly promising direction to strictly enforce this efficiency is to quantitatively monitor the reasoning content. By calculating the embedding similarity between consecutive reasoning steps or employing an LLM-as-a-Judge, future iterations can explicitly filter out repetitive SFT trajectories or design targeted RLVR penalties, pushing the boundary of cognitive efficiency even further.
+
+![overview](https://cdn-uploads.huggingface.co/production/uploads/669f53549a21428ccda89fab/YQmyhamGa29mmAWAh1Qoe.png)
+
+![swe-bench-verified](https://cdn-uploads.huggingface.co/production/uploads/669f53549a21428ccda89fab/YGIov1OkGhAcE4wsPBCs2.png)
+
+## ⭐️ Citation
+
+If you find this project useful, please cite our work:
+
+```bibtex
+@misc{lian2026sweagilesoftwareagentframework,
+      title={SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context}, 
+      author={Shuquan Lian and Juncheng Liu and Yazhe Chen and Yuhong Chen and Hui Li},
+      year={2026},
+      eprint={2604.11716},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2604.11716}, 
+}
+```
+
+## 🤝 Acknowledgements
+
+We sincerely thank the projects [R2E-Gym/R2E-Gym](https://github.com/R2E-Gym/R2E-Gym) and [rllm-org/rllm](https://github.com/rllm-org/rllm) for providing their open-source resources.