qwq-plus

The enhanced QwQ reasoning model, based on the Qwen2.5 model, has been significantly improved in its reasoning capabilities through reinforcement learning. It focuses on solving deep-level problems in mathematics and programming.

• Deep introspective reasoning: By simulating the human “think—question—revise” cycle, it enables step-by-step decomposition and verification of complex problems, greatly enhancing solution accuracy.
• Outstanding mathematical and coding abilities: It performs exceptionally well in multiple high-difficulty evaluations—
• MATH-500: 90.6% (comprehensively covers algebra, geometry, number theory, and more)
• AIME: 50.0% (reaches a high level typical of middle-school math competitions)
• GPQA: 65.2% (demonstrates graduate-level scientific reasoning ability)
• LiveCodeBench: 50.0% (strong capability in generating code for real-world programming scenarios)

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Core Capabilities

🧠 Chained self-dialogue: During problem-solving, it proactively questions assumptions and examines intermediate steps (such as the deduction process in the logic puzzle “Square 5”), avoiding blind output.

🧮 Formalized thinking: It excels at tasks requiring rigorous logical chains, such as mathematical proofs, algorithm design, and symbolic computation.

💻 Practical programming skills: It can generate clearly structured, functionally correct code and explain its implementation思路.

🔍 Research-oriented exploration: As an experimental model, it aims to explore how AI can think like humans, driving the advancement of reasoning-based AI.