LongCat-Flash-Thinking - An Efficient Reasoning Model for Meituan Open Source

What is LongCat-Flash-Thinking?

LongCat-Flash-Thinking is an efficient reasoning model released by the LongCat team of Meituan, which has become more powerful and professional while maintaining the extreme speed of LongCat-Flash-Chat. The model has reached the leading level of global open source models in reasoning tasks in multiple domains such as logic, math, code, and intelligences. It has "deep thinking + tool invocation" and "non-formal + formal" reasoning capabilities. Its domain-parallel reinforcement learning training method achieves balanced improvement of model capability by decoupling optimization in different domains. The asynchronous elastic co-card system (DORA) then provides support for training and realizes efficient training. LongCat-Flash-Thinking has set new records in many authoritative reviews, demonstrating excellent general reasoning, math, code, intelligences, and ATP formal reasoning capabilities.

Features of LongCat-Flash-Thinking

• Powerful reasoning: LongCat-Flash-Thinking excels in multi-domain reasoning tasks in logic, math, code, intelligences, etc., and reaches the leading level of global open source models.
• Deep thinking combined with tool invocation: Ability to think deeply while autonomously invoking tools (e.g., code executors, APIs, etc.) to efficiently solve complex tasks.
• Integration of non-formal and formal reasoning: It is the first large language model in China that has both reasoning capabilities, enhancing its reliability in academic and engineering applications.
• Efficient training and optimization: A domain-parallel reinforcement learning training method is used to decouple different domain optimizations and achieve balanced model capability improvement; the asynchronous elastic co-card system (DORA) supports efficient training.
• Excellent performance: Setting new records in a number of authoritative reviews, such as surpassing the top closed-source models in the ARC-AGI benchmarks, and demonstrating strong competitiveness in benchmarks for math, code, and intelligentsia.
• Open Source and ApplicationsThe model has been fully open-sourced on HuggingFace and GitHub to facilitate developers' use and research and to advance the technology.

LongCat-Flash-Thinking's Core Advantages

• Domain Parallel Reinforcement Learning Training Methods: By decoupling the optimization of different domains, we achieve a balanced increase in the model's capability, and the comprehensive performance reaches the Pareto optimum.
• Asynchronous Resilient Common Card System (DORA): Achieve efficient asynchronous training, accelerate the training process, and at the same time ensure the consistency of the sample strategy to support the stable operation of large-scale clusters.
• A Reasoning Framework for Dual Path Intelligentsia: Autonomous screening of optimal query samples combined with intelligent body reasoning and tool usage to significantly optimize resource utilization for complex tasks.
• A framework for formalized reasoning: Generating rigorously verified proof processes based on an expert iterative framework to systematically enhance the formal reasoning of models.

What is LongCat-Flash-Thinking's official website?

• Official website experience::LongCat Open Platform
• GitHub repository:: https://github.com/meituan-longcat/LongCat-Flash-Thinking
• HuggingFace Model Library:: https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking
• Technical Papers:: https://github.com/meituan-longcat/LongCat-Flash-Thinking/blob/main/tech_report.pdf

Who is LongCat-Flash-Thinking for?

• (scientific) researcher: Can be used for cutting-edge research to explore inference mechanisms and optimization methods for models.
• developers: It can be integrated into various types of applications to enhance the intelligence of the applications, such as developing intelligent assistants and automation tools.
• business user: For companies that need efficient reasoning solutions that optimize business processes and improve decision-making.
• educator: Can be used in teaching and research to help students understand complex reasoning processes and modeling applications.
• technology enthusiast: Interested in new technologies to explore the potential of models for innovation and experimentation.