6 papers
KAT-Coder-V2.5 Technical Report
Bo Huang, Fengxiang Li, Hao Xu +50
We present KAT-Coder-V2.5, a coding-focused agentic model trained to act autonomously inside real, executable repositories rather than as a single-turn code generator. Its capabili…
Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin
Hanyang Li, Jianhao Ma, Ying Cui
Post-training quantization (PTQ) converts a trained full-precision model into low-bit weights without task-level retraining, while quantization-aware training (QAT) incorporates qu…
Tree Training: Accelerating Agentic LLMs Training via Shared Prefix Reuse
Jinghui Wang, Shaojie Wang, Yinghan Cui +9
Agentic large language model (LLM) training often involves multi-turn interaction trajectories that branch into multiple execution paths due to concurrent tool use, think-mode, sub…
KAT-Coder-V2 Technical Report
Fengxiang Li, Han Zhang, Haoyang Huang +43
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 in…
SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling
Jinghui Wang, Shaojie Wang, Yinghan Cui +24
We introduce SeamlessFlow, a server based reinforcement learning (RL) framework that addresses two core challenges in industrial scale RL: (1) decoupling RL training from the compl…
SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM
Xiaojiang Zhang, Jinghui Wang, Zifei Cheng +14
Recent advances of reasoning models, exemplified by OpenAI's o1 and DeepSeek's R1, highlight the significant potential of Reinforcement Learning (RL) to enhance the reasoning capab…