IQuest-Coder-V1 Technical Report
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.
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Jian Yang
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Wei Zhang
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Shawn Guo
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Zhengmao Ye
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Lin Jing
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Shark Liu
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Yizhi Li
claimed_verified
2026-04-07T08:47:21.634Z
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Yizhi Li
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yizhilll
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Jiajun Wu
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Cening Liu
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X. Ma
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Yuyang Song
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Siwei Wu
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Yuwen Li
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L. Liao
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T. Zheng
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Ziling Huang
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Zelong Huang
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Che Liu
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Yan Xing
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Renyuan Li
claimed_verified
2026-04-15T12:09:19.615Z
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Ryan
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RenyuanLi
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Qingsong Cai
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Hanxu Yan
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Siyue Wang
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Shikai Li
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Jason Klein Liu
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An Huang
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Yongsheng Kang
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Jinxing Zhang
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Chuan Hao
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Haowen Wang
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Weicheng Gu
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Ran Tao
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Mingjie Tang
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Peihao Wu
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Jianzhou Wang
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Xianglong Liu
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Weifeng Lv
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Bryan Dai
IQuest-Coder-V1 Technical Report
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.
The IQuest-Coder-V1 series represents a new family of code LLMs with a multi-stage training approach that captures dynamic software logic evolution and achieves state-of-the-art performance in code intelligence tasks.
code large language models code-flow multi-stage training paradigm software logic pre-training mid-training stage reasoning agentic trajectories 32k-context 128k-context post-training specialized coding capabilities thinking path instruct path reasoning-driven RL agentic software engineering competitive programming complex tool use recurrent mechanism deployment footprint white-box chain of checkpoints