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Model: FutureLivingLab/iFlow-ROME Source: Original Platform
2026-09-01 04:24:18 +08:00
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+arxiv: 2512.24873
+tags:
+- agent
+- moe
+---
+
+# ROME-30B-A3B
+
+<p align="left" style="display: flex; gap: 8px; align-items: center;">
+  <a href="https://arxiv.org/pdf/2512.24873" target="_blank">
+    <img src="https://img.shields.io/badge/Paper-arXiv%3A2512.24873-red" alt="Paper">
+  </a>
+  <a href="https://faithful-almanac-add.notion.site/The-Bitter-Lesson-Behind-Building-Agentic-RL-in-Terminal-Environments-2eaddd45837f80c9ad2ed6a15ef3c1a1?pvs=74" target="_blank">
+    <img src="https://img.shields.io/badge/Blog-Notion-orange" alt="Blog">
+  </a>
+  <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License">
+  <img src="https://img.shields.io/badge/Model%20Type-MoE-green" alt="Model Type">
+</p>
+
+
+
+---
+
+
+**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.
+
+<img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/performance.png" width="600"/>
+
+
+---
+
+
+
+## 🚀 Highlights
+
+<img src="https://rlhf.oss-cn-hangzhou.aliyuncs.com/iFLOW-ROME/ALE.PNG" width="600"/>
+
+
+### 🔧 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}
+}
+```