IQuest-Coder-V1 Technical Report
Authors: name: Jian Yang, name: Wei Zhang, name: Shawn Guo, name: Zhengmao Ye, name: Lin Jing, name: Shark Liu, name: Yizhi Li, name: Jiajun Wu, name: Cening Liu, name: X. Ma, name: Yuyang Song, name: Siwei Wu, name: Yuwen Li, name: L. Liao, name: T. Zheng, name: Ziling Huang, name: Zelong Huang, name: Che Liu, name: Yan Xing, name: Renyuan Li, name: Qingsong Cai, name: Hanxu Yan, name: Siyue Wang, name: Shikai Li, name: Jason Klein Liu, name: An Huang, name: Yongsheng Kang, name: Jinxing Zhang, name: Chuan Hao, name: Haowen Wang, name: Weicheng Gu, name: Ran Tao, name: Mingjie Tang, name: Peihao Wu, name: Jianzhou Wang, name: Xianglong Liu, name: Weifeng Lv, name: Bryan Dai
Organization: IQuest

In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we propose the code-flow multi-stage training paradigm, which captures the dynamic evolution of software logic through different phases of the pipeline. Our models are developed through the evolutionary pipeline, starting with the initial pre-training consisting of code facts, repository, and completion data. Following that, we implement a specialized mid-training stage that integrates reasoning and agentic trajectories in 32k-context and repository-scale in 128k-context to forge deep logical foundations. The models are then finalized with post-training of specialized coding capabilities, which is bifurcated into two specialized paths: the thinking path (utilizing reasoning-driven RL) and the instruct path (optimized for general assistance). IQuest-Coder-V1 achieves state-of-the-art performance among competitive models across critical dimensions of code intelligence: agentic software engineering, competitive programming, and complex tool use. To address deployment constraints, the IQuest-Coder-V1-Loop variant introduces a recurrent mechanism designed to optimize the trade-off between model capacity and deployment footprint, offering an architecturally enhanced path for efficacy-efficiency trade-off. We believe the release of the IQuest-Coder-V1 series, including the complete white-box chain of checkpoints from pre-training bases to the final thinking and instruction models, will advance research in autonomous code intelligence and real-world agentic systems.