108 lines
3.6 KiB
Markdown
108 lines
3.6 KiB
Markdown
---
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license: apache-2.0
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pipeline_tag: text-generation
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arxiv: 2512.24873
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tags:
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- agent
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- moe
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---
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# ROME-30B-A3B
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<p align="left" style="display: flex; gap: 8px; align-items: center;">
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<a href="https://arxiv.org/pdf/2512.24873" target="_blank">
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<img src="https://img.shields.io/badge/Paper-arXiv%3A2512.24873-red" alt="Paper">
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</a>
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<a href="https://faithful-almanac-add.notion.site/The-Bitter-Lesson-Behind-Building-Agentic-RL-in-Terminal-Environments-2eaddd45837f80c9ad2ed6a15ef3c1a1?pvs=74" target="_blank">
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<img src="https://img.shields.io/badge/Blog-Notion-orange" alt="Blog">
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</a>
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<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License">
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<img src="https://img.shields.io/badge/Model%20Type-MoE-green" alt="Model Type">
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</p>
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---
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**ROME** (**R**OME is **O**bviously an **A**gentic **M**odEl) 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 performance through full-stack infrastructure integration and advanced Reinforcement Learning optimization.
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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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<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**](https://github.com/alibaba/ROLL) – Large-scale reinforcement learning optimization engine
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- [**ROCK**](https://github.com/alibaba/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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## 📜 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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}
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``` |