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  ## 📢 Note: Coming Soon!
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  **ROME (ROME is Obviously an Agentic ModEl)** will be officially released soon.
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- The project is currently under final review and preparation. Model weights will be made publicly available shortly. *(The iFlow CLI has already been released.)* Stay tuned!
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  <img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/performance.png" width="600"/>
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  ## Highlights
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- **ROME** is an open-source **agentic foundation model** incubated within the **ALE (Agentic Learning Ecosystem)**.
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- Rather than scaling performance purely by increasing parameter count, ROME achieves *parameter-scale–crossing agentic performance* through full-stack infrastructure and algorithmic optimization.
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  <img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/ALE.PNG" width="600"/>
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  | **Model** | **Terminal-Bench 2.0** | **SWE-bench Verified** |
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  | ---------------------------- | ---------------------- | ---------------------- |
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  | Qwen3-Coder-30B-A3B-Instruct | 13.48% | 46.33% |
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- | **ROME (30B-A3B)** | **24.72%** | **57.40%** |
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  | GPT-OSS-120B | 21.12% | 43.93% |
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  | GLM-4.5 Air (106B) | 17.30% | 56.20% |
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@@ -100,7 +100,7 @@ If you find our work useful, please consider citing:
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  ```bibtex
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  @article{rome2025ale,
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  title={Let It Flow: Agentic Crafting on Rock and Roll - Building the ROME Model within an Open Agentic Learning Ecosystem},
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- author={ROME & ALE Team},
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  journal={arXiv preprint arXiv:2512.24873},
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  year={2025}
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  }
 
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  ## 📢 Note: Coming Soon!
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  **ROME (ROME is Obviously an Agentic ModEl)** will be officially released soon.
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+ The project is currently under final review and preparation. Model weights will be made publicly available shortly. Stay tuned!
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  <img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/performance.png" width="600"/>
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  ## Highlights
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+ **ROME** is an open-source **agentic model** incubated within the **ALE (Agentic Learning Ecosystem)**.
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+ Rather than scaling performance purely by increasing parameter count, ROME achieves parameter-scale–crossing agentic performance through full-stack infrastructure and RL algorithmic optimization.
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  <img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/ALE.PNG" width="600"/>
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  | **Model** | **Terminal-Bench 2.0** | **SWE-bench Verified** |
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  | ---------------------------- | ---------------------- | ---------------------- |
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  | Qwen3-Coder-30B-A3B-Instruct | 13.48% | 46.33% |
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+ | **ROME-30B-A3B** | **24.72%** | **57.40%** |
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  | GPT-OSS-120B | 21.12% | 43.93% |
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  | GLM-4.5 Air (106B) | 17.30% | 56.20% |
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  ```bibtex
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  @article{rome2025ale,
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  title={Let It Flow: Agentic Crafting on Rock and Roll - Building the ROME Model within an Open Agentic Learning Ecosystem},
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+ author={Wang, Weixun and Xu, XiaoXiao and An, Wanhe and Dai, Fangwen and others},
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  journal={arXiv preprint arXiv:2512.24873},
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  year={2025}
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  }