--- license: apache-2.0 pipeline_tag: text-generation arxiv: 2512.24873 tags: - agent - moe --- # ROME-30B-A3B

--- **ROME** (**R**OME is **O**bviously an **A**gentic **M**odEl) is an open-source **agentic model** incubated within the **ALE (Agentic Learning Ecosystem)**. Rather than scaling performance purely by increasing parameter count, ROME achieves parameter-scale–crossing performance through full-stack infrastructure integration and advanced Reinforcement Learning optimization.

--- ## 🚀 Highlights

### 🔧 ALE Full-Stack Infrastructure - [**ROLL**](https://github.com/alibaba/ROLL) – Large-scale reinforcement learning optimization engine - [**ROCK**](https://github.com/alibaba/ROCK) – Secure sandbox and environment orchestration for agent execution - **iFlow CLI** – Unified agent framework and developer interface ### 🧠 IPA Policy Optimization Algorithm - Introduces **Interaction-Perceptive Agentic Policy Optimization (IPA)** - Performs credit assignment at the level of **Semantic Interaction Chunks** - Significantly improves **training stability** and **success rates** on **long-horizon tasks** ### 🚀 Strong Agentic Performance - Despite being a **mid-sized model** (30B MoE with 3B active parameters), ROME outperforms same-scale models on standard agent benchmarks: - **Terminal-Bench 2.0**: 24.72% - **SWE-bench Verified**: 57.40% - Performance is competitive with, and in some cases comparable to, models exceeding **100B parameters** ### 🔒 Production-Grade Safety - Designed for autonomous agent execution in real environments - Rigorously aligned and red-teamed against risks such as: - Unauthorized access - Illegal or unsafe tool invocation - Built with **deployment-grade safety guarantees** in mind --- ## 📊 Performance (Preview) ### Terminal-Based Benchmarks | **Model** | **Terminal-Bench 2.0** | **SWE-bench Verified** | | ---------------------------- | ---------------------- | ---------------------- | | Qwen3-Coder-30B-A3B-Instruct | 13.48% | 46.33% | | **ROME-30B-A3B** | **24.72%** | **57.40%** | | GPT-OSS-120B | 21.12% | 43.93% | | GLM-4.5 Air (106B) | 17.30% | 56.20% | > See the technical report for full experimental details. --- ## 📜 Citation If you find our work useful, please consider citing: ```bibtex @article{rome2025ale, title={Let It Flow: Agentic Crafting on Rock and Roll - Building the ROME Model within an Open Agentic Learning Ecosystem}, author={Wang, Weixun and Xu, XiaoXiao and An, Wanhe and Dai, Fangwen and others}, journal={arXiv preprint arXiv:2512.24873}, year={2025} } ```