KAT-Coder-V2 Technical Report
We present KAT-Coder-V2, an agentic coding model developed by the KwaiKAT team at Kuaishou. KAT-Coder-V2 adopts a "Specialize-then-Unify" paradigm that decomposes agentic coding into five expert domains - SWE, WebCoding, Terminal, WebSearch, and General - each undergoing independent supervised fine-tuning and reinforcement learning, before being consolidated into a single model via on-policy distillation. We develop KwaiEnv, a modular infrastructure sustaining tens of thousands of concurrent sandbox instances, and scale RL training along task complexity, intent alignment, and scaffold generalization. We further propose MCLA for stabilizing MoE RL training and Tree Training for eliminating redundant computation over tree-structured trajectories with up to 6.2x speedup. KAT-Coder-V2 achieves 79.6% on SWE-bench Verified (vs. Claude Opus 4.6 at 80.8%), 88.7 on PinchBench (surpassing GLM-5 and MiniMax M2.7), ranks first across all three frontend aesthetics scenarios, and maintains strong generalist scores on Terminal-Bench Hard (46.8) and tau^2-Bench (93.9). Our model is publicly available at https://streamlake.com/product/kat-coder.
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Fengxiang Li
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Han Zhang
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Haoyang Huang
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Jinghui Wang
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Jinhua Hao
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Kun Yuan
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Mengtong Li
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Minglei Zhang
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Pengcheng Xu
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Wenhao Zhuang
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Yizhen Shao
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Zongxian Feng
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Can Tang
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Chao Wang
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Chengxiao Tong
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Fan Yang
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Gang Xiong
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Haixuan Gao
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Han Gao
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Hao Wang
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Haochen Liu
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Hongliang Sun
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Jiabao Li
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Jiabao Li
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Jingwen Chang
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2026-03-31T10:03:03.360Z
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JingwenChang
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Chang992
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Jun Du
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Junyi Peng
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Leizhen Cui
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Meimei Jing
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Mingqi Wu
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Mingqi Wu
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wumingqi
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Shangpeng Yan
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Shaotong Qi
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Suzhe Xu
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Wenxuan Zhao
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Xianda Sun
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Xuan Xie
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Yanbo Wang
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Yao Xia
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Yinghan Cui
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Yingpeng Chen
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Yong Wang
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Yuze Shi
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Zhiwei Shen
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Ziyu Wang
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Ming Sun
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Lin Ye
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Bin Chen
KAT-Coder-V2 Technical Report
We present KAT-Coder-V2, an agentic coding model developed by the KwaiKAT team at Kuaishou. KAT-Coder-V2 adopts a "Specialize-then-Unify" paradigm that decomposes agentic coding into five expert domains - SWE, WebCoding, Terminal, WebSearch, and General - each undergoing independent supervised fine-tuning and reinforcement learning, before being consolidated into a single model via on-policy distillation. We develop KwaiEnv, a modular infrastructure sustaining tens of thousands of concurrent sandbox instances, and scale RL training along task complexity, intent alignment, and scaffold generalization. We further propose MCLA for stabilizing MoE RL training and Tree Training for eliminating redundant computation over tree-structured trajectories with up to 6.2x speedup. KAT-Coder-V2 achieves 79.6% on SWE-bench Verified (vs. Claude Opus 4.6 at 80.8%), 88.7 on PinchBench (surpassing GLM-5 and MiniMax M2.7), ranks first across all three frontend aesthetics scenarios, and maintains strong generalist scores on Terminal-Bench Hard (46.8) and tau^2-Bench (93.9). Our model is publicly available at https://streamlake.com/product/kat-coder.
KAT-Coder-V2 employs a specialized-agentic approach with domain-specific fine-tuning and reinforcement learning, enhanced by novel training methods and infrastructure for improved code generation performance.
agentic coding model Specialize-then-Unify paradigm supervised fine-tuning reinforcement learning on-policy distillation modular infrastructure sandbox instances MoE RL training MCLA Tree Training SWE-bench Verified PinchBench Terminal-Bench Hard tau^2-Bench