QwQ: Reflect Deeply on the Boundaries of the Unknown
- Source
- qwenlm.github.io
- Author
- Qwen Team
- Date

Context
QwQ-32B-Preview explored answering difficult questions through extended intermediate reasoning. Qwen reported 90.6% on MATH-500, a set of 500 mathematics problems, in its November 2024 release. The same account describes circular reasoning that can produce long responses without a conclusion, alongside weaknesses in common-sense reasoning and nuanced language understanding. Together, those findings make the preview useful as a research model for technical problem solving. They also explain why the length or reflective tone of an answer is an unreliable substitute for checking whether it reaches a valid result.
- reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
“Like an eternal student of wisdom, it approaches every problem - be it mathematics, code, or knowledge of our world - with genuine wonder and doubt.”
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