What is KAT-V1
KAT-V1 is Kuaishou's open-source AutoThink large model, available in 40B and 200B versions. The model combines thinking and non-thinking capabilities, and can automatically switch thinking modes based on question difficulty. The 40B version's performance approaches DeepSeek-R1 (685 billion parameters), while the 200B version surpasses open-source models such as Qwen, DeepSeek, and Llama in multiple benchmark tests. KAT-V1 uses a hybrid long-short thinking training paradigm and a novel reinforcement learning method, Step-SRPO, to improve thinking density and judgment, and efficiently completes cold start through a heterogeneous distillation framework. The model performs excellently in complex reasoning tasks such as code generation and SQL optimization, and supports user-guided thinking modes.
KAT-V1's Main Features
- Automatic switching between thinking and non-thinking modes: Automatically determines whether to enter thinking mode based on the complexity of the question. For complex questions, the model initiates deep reasoning and planning; for simple questions, it directly gives the answer, avoiding unnecessary waste of computational resources.
- Complex reasoning capability: When facing complex programming tasks (such as generating code that simulates a small ball moving inside a rotating hexagon) and tasks such as SQL optimization, KAT-V1 provides structured multi-step analysis and solutions.
- Multi-turn dialogue capability: KAT-V1 supports multi-turn dialogue, gradually improving solutions according to user needs.
- User intent guidance: The model supports users guiding whether to enable thinking mode with simple intent instructions (such as explicit thinking or non-thinking preferences).
- Agent mode adaptation: Adapts to multi-agent scenarios, for example, disabling reasoning during file checks and enabling deep reasoning when diagnosis or code generation is needed.
KAT-V1's Technical Principles
- Hybrid long-short thinking model training paradigm: KAT-V1 introduces a new hybrid long-short thinking model training paradigm, combining the traditional reinforcement learning algorithm (GRPO) and the novel reinforcement learning method Step-SRPO. This paradigm improves the model's thinking density of output tokens and its ability to judge whether thinking mode should be enabled.
- Heterogeneous distillation framework: KAT-V1 uses a unique heterogeneous distillation framework, including two major modules: the universal Logits distillation loss (ULD Loss) and multi-token prediction (MTP). In this way, the model completes cold start at a lower cost while improving the efficiency of knowledge transfer.
- Step-SRPO reinforcement learning algorithm: The Step-SRPO algorithm guides model learning through a dual reward mechanism (judgment reward and answer reward), allowing the model to gradually learn during training to flexibly adjust reasoning depth according to question difficulty, thereby further reducing token usage while model performance improves.
- High-quality data synthesis: In the pretraining stage, a large amount of high-quality thinking/non-thinking data is used. The thinking data is synthesized through an Agentic framework, which consists of a solver, a thinker, and a reviewer, ensuring the logical consistency and output quality of the synthesized data.
KAT-V1's Project Address
- HuggingFace Model Library：https://huggingface.co/Kwaipilot/KAT-V1-40B
- arXiv Technical Paper：https://arxiv.org/pdf/2507.08297
KAT-V1's Application Scenarios
- Code generation and optimization: KAT-V1 can generate complex code, such as Python code that simulates a small ball moving inside a rotating hexagon, and provide SQL optimization suggestions.
- Complex reasoning and problem solving: Automatically judges the difficulty of a question, initiates deep reasoning mode, and supports multi-turn dialogue to gradually improve solutions.
- Multi-agent scenarios: The model adapts to multi-agent scenarios, supporting agent collaboration and task allocation, such as file checking and code generation.
- User intent guidance: Supports users guiding the model through simple instructions on whether to enable thinking mode, providing personalized services.
- Multimodal and interactive applications: Expected to expand to multimodal applications in the future, supporting real-time interaction and dynamic adjustment.