license: apache-2.0
pipeline_tag: text-generation
arxiv: 2512.24873
tags:
- agent
- moe
ROME-30B-A3B (Coming Soon)
🔗 Technical Report
📢 Note: Coming Soon!
ROME (ROME is Obviously an Agentic ModEl) will be officially released soon. The project is currently under final review and preparation. Model weights will be made publicly available shortly. Stay tuned!
Highlights
ROME 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 agentic performance through full-stack infrastructure and RL algorithmic optimization.
🔧 ALE Full-Stack Infrastructure
ROLL – Large-scale reinforcement learning optimization engine
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.
Best Practices
(Code examples and usage guidelines will be added after the model release.)
Citation
If you find our work useful, please consider citing:
@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}
}