iFlow-ROME Explained: How Alibaba’s 30B AI Agent Mastered Real-World Coding Tasks

iFlow-ROME: A Complete Guide to Alibaba’s Next-Generation AI Agent Training System Snippet Summary: iFlow-ROME is Alibaba’s agentic learning ecosystem featuring a 30B MoE ROME model that achieves 57.40% task completion on SWE-bench Verified. The system generates over 1 million verified interaction trajectories through ROCK sandbox manager and employs a three-stage curriculum training methodology for end-to-end execution optimization in real-world environments. When you type a command in your terminal, expecting AI to help you complete complex software engineering tasks, traditional large language models often disappoint—they might generate code that looks reasonable but crashes when you run it, or they “lose the …