ROME-30B-A3B

ROME (ROME is Obviously an Agentic ModEl) 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 – 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

See the technical report for full experimental details.

📜 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}
}