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
Published: 2025-12-31 14:03:39

Abstract: 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.