Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
Authors: name: Weixun Wang, name: XiaoXiao Xu, name: Wanhe An, name: Fangwen Dai, name: Wei Gao, name: Yancheng He, name: Ju Huang, name: Qiang Ji, name: Hanqi Jin, name: Xiaoyang Li, name: Yang Li, name: Zhongwen Li, name: Shirong Lin, name: Jiashun Liu, name: Zenan Liu, name: Tao Luo, name: Dilxat Muhtar, name: Yuanbin Qu, name: Jiaqiang Shi, name: Qinghui Sun, name: Yingshui Tan, name: Hao Tang, name: Runze Wang, name: Yi Wang, name: Zhaoguo Wang, name: Yanan Wu, name: Shaopan Xiong, name: Binchen Xu, name: Xander Xu, name: Yuchi Xu, name: Qipeng Zhang, name: Xixia Zhang, name: Haizhou Zhao, name: Jie Zhao, name: Shuaibing Zhao, name: Baihui Zheng, name: Jianhui Zheng, name: Suhang Zheng, name: Yanni Zhu, name: Mengze Cai, name: Kerui Cao, name: Xitong Chen, name: Yue Dai, name: Lifan Du, name: Tao Feng, name: Tao He, name: Jin Hu, name: Yijie Hu, name: Ziyu Jiang, name: Cheng Li, name: Xiang Li, name: Jing Liang, name: Chonghuan Liu, name: ZhenDong Liu, name: Haodong Mi, name: Yanhu Mo, name: Junjia Ni, name: Shixin Pei, name: Jingyu Shen, name: XiaoShuai Song, name: Cecilia Wang, name: Chaofan Wang, name: Kangyu Wang, name: Pei Wang, name: Tao Wang, name: Wei Wang, name: Ke Xiao, name: Mingyu Xu, name: Tiange Xu, name: Nan Ya, name: Siran Yang, name: Jianan Ye, name: Yaxing Zang, name: Duo Zhang, name: Junbo Zhang, name: Boren Zheng, name: Wanxi Deng, name: Ling Pan, name: Lin Qu, name: Wenbo Su, name: Jiamang Wang, name: Wei Wang, name: Hu Wei, name: Minggang Wu, name: Cheng Yu, name: Bing Zhao, name: Zhicheng Zheng, name: Bo Zheng
Organization: AGI Lab

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agent LLMs. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME (ROME is Obviously an Agentic Model), an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-based Policy Alignment (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.