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license: apache-2.0 |
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pipeline_tag: text-generation |
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# ROME-30B-A3B (Coming Soon) |
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<a href="https://arxiv.org/pdf/2512.24873" target="_blank"> |
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🔗 <strong>Technical Report</strong><br/> |
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<img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv%3A2512.24873-red"/> |
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</a> |
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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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--- |
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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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### 🔧 ALE Full-Stack Infrastructure |
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- **ROLL** – Large-scale reinforcement learning optimization engine |
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- **ROCK** – Secure sandbox and environment orchestration for agent execution |
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- **iFlow CLI** – Unified agent framework and developer interface |
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### 🧠 IPA Policy Optimization Algorithm |
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- Introduces **Interaction-Perceptive Agentic Policy Optimization (IPA)** |
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- Performs credit assignment at the level of **Semantic Interaction Chunks** |
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- Significantly improves **training stability** and **success rates** on **long-horizon tasks** |
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### 🚀 Strong Agentic Performance |
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- Despite being a **mid-sized model** (30B MoE with 3B active parameters), ROME outperforms same-scale models on standard agent benchmarks: |
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- **Terminal-Bench 2.0**: 24.72% |
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- **SWE-bench Verified**: 57.40% |
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- Performance is competitive with, and in some cases comparable to, models exceeding **100B parameters** |
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### 🔒 Production-Grade Safety |
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- Designed for autonomous agent execution in real environments |
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- Rigorously aligned and red-teamed against risks such as: |
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- Unauthorized access |
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- Illegal or unsafe tool invocation |
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- Built with **deployment-grade safety guarantees** in mind |
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--- |
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## Performance (Preview) |
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### Terminal-Based Benchmarks |
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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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> See the technical report for full experimental details. |
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--- |
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## Best Practices |
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*(Code examples and usage guidelines will be added after the model release.)* |
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--- |
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## Citation |
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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={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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} |
