LongCat-Flash-Thinking - Meituan's Large-Scale Inference Model

What is LongCat-Flash-Thinking?

LongCat-Flash-Thinking is a large-scale inference model with 560 billion parameters, developed by Meituan's LongCat team. Based on a hybrid expert (MoE) architecture, it supports dynamic activation of 18.6 billion to 31.3 billion parameters as needed, balancing computational efficiency and performance. The model undergoes two-stage training: long-chain cold-start training and large-scale reinforcement learning. It possesses powerful formal reasoning and agent-based reasoning capabilities, performing exceptionally well in complex reasoning tasks such as mathematics, logic, and programming. It can provide efficient intelligent support for scientific research, software development, and enterprise decision-making, promoting the widespread adoption of AI technology.

Main functions of LongCat-Flash-Thinking

• Complex ReasoningThe model excels at handling complex reasoning tasks such as mathematics, logic, and programming, including automatic theorem proving and solving difficult problems in the International Mathematical Olympiad.
• Intelligent agent tool usageThe model can flexibly utilize external tools to assist in solving problems and improve task execution efficiency. For example, when multiple steps are required or specific functions need to be called, the appropriate tools can be automatically selected and used.
• General Questions and Answers and DialoguesIt possesses strong general language understanding and generation capabilities, can conduct fluent natural language dialogues, answer questions in various fields, and provide information consulting services.
• Formal ReasoningIt excels in rigorous logical proofs and formal language processing, supporting applications in fields such as mathematics and physics that require precise logical verification.

The technical principles of LongCat-Flash-Thinking

• Expert Hybrid (MoE) ArchitectureIt has 560 billion parameters, and dynamically activates 18.6 billion to 31.3 billion parameters based on context, achieving a balance between computational efficiency and performance.
• Two-stage training process:
  • Long Mind Chain Cold Start TrainingThe basic reasoning ability of the model is cultivated through course learning strategies and the SFT stage of reasoning-intensive data.
  • Large-scale reinforcement learningAsynchronous reinforcement learning is performed based on the DORA system, and a domain-parallel training scheme is adopted to decouple and then fuse the optimization processes of different domains, thereby improving the model's inference ability and generalization performance.
• DORA systemAs a reinforcement learning infrastructure, it adopts a streaming architecture, multi-version design, and elastic co-location strategy to support efficient and stable asynchronous training on tens of thousands of accelerators, thereby improving training efficiency and model performance.

LongCat-Flash-Thinking project address

• GitHub repository: https://github.com/meituan-longcat/LongCat-Flash-Thinking
• HuggingFace model libraryhttps://huggingface.co/meituan-longcat/LongCat-Flash-Thinking
• Technical Papers: https://github.com/meituan-longcat/LongCat-Flash-Thinking/blob/main/tech_report.pdf

Application scenarios of LongCat-Flash-Thinking

• Software developmentIn software development, it can automatically generate code snippets, quickly locate and fix errors in the code, and significantly improve development efficiency and code quality.
• Scientific researchResearchers use it to prove mathematical theorems, derive physical models, and analyze complex data, helping to accelerate the research process and improve research efficiency.
• Corporate DecisionsAnalyze market data in the enterprise environment to provide strategic planning and investment advice, helping companies make smarter decisions and enhance their competitiveness.
• EducationIn education, it provides personalized learning plans based on students' learning progress, answers students' questions in real time, assists teachers in generating teaching resources, and improves teaching effectiveness.
• HealthcareIn the healthcare field, it assists doctors in making medical diagnoses, managing medical knowledge bases, providing patients with personalized health management advice, and improving the quality and efficiency of medical services.