|
|
--- |
|
|
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> |
|
|
<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} |
|
|
} |
|
|
``` |
