Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
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.
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Weixun Wang
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XiaoXiao Xu
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Wanhe An
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Fangwen Dai
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Wei Gao
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Yancheng He
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Ju Huang
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Qiang Ji
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Hanqi Jin
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Xiaoyang Li
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Yang Li
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Yang Li (SJTU & SII)
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Zhongwen Li
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Shirong Lin
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Jiashun Liu
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Zenan Liu
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Tao Luo
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Dilxat Muhtar
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Yuanbin Qu
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Jiaqiang Shi
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Qinghui Sun
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SunQinghui
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Yingshui Tan
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Hao Tang
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Runze Wang
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Yi Wang
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Zhaoguo Wang
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Yanan Wu
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Shaopan Xiong
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Binchen Xu
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Xander Xu
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Yuchi Xu
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Qipeng Zhang
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Xixia Zhang
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Haizhou Zhao
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Jie Zhao
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Shuaibing Zhao
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Baihui Zheng
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Jianhui Zheng
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Suhang Zheng
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Kerui Cao
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Xitong Chen
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Yue Dai
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Lifan Du
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Tao Feng
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Tao He
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Jin Hu
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Yijie Hu
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Ziyu Jiang
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Williams JIANG
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Cheng Li
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Xiang Li
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Jing Liang
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Chonghuan Liu
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ZhenDong Liu
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Haodong Mi
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Junjia Ni
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Pei Wang
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Wei Wang
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Ke Xiao
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Mingyu Xu
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Tiange Xu
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Nan Ya
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Siran Yang
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Jianan Ye
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Yaxing Zang
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Duo Zhang
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Junbo Zhang
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Boren Zheng
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Wanxi Deng
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Ling Pan
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Lin Qu
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Wenbo Su
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Jiamang Wang
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Wei Wang
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Hu Wei
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Minggang Wu
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Cheng Yu
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Bo Zheng
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Bo Zheng
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Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
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.
The Agentic Learning Ecosystem (ALE) introduces a principled infrastructure for agent development, combining post-training optimization, sandbox environments, and policy alignment to enhance long-horizon training stability and performance in real-world tasks.
ROLL (post-training framework) ROCK (sandbox environment manager) iFlow CLI (agent framework) ROME (agentic model) data composition protocols Interaction-based Policy Alignment (IPA) semantic interaction chunks Terminal Bench Pro SWE-bench Verified