Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More<\/em><\/p>\n\n\n\n Qwen Team, a division of Chinese e-commerce giant Alibaba developing its growing family of open-source Qwen large language models (LLMs), has introduced QwQ-32B, a new 32-billion-parameter reasoning model designed to improve performance on complex problem-solving tasks through reinforcement learning (RL).<\/p>\n\n\n\n The model is available as open-weight on Hugging Face and on ModelScope under an Apache 2.0 license. This means it\u2019s available for commercial and research uses, so enterprises can employ it immediately to power their products and applications (even ones they charge customers to use).<\/p>\n\n\n\n It can also be accessed for individual users via Qwen Chat.<\/p>\n\n\n\n QwQ, short for Qwen-with-Questions, was first introduced by Alibaba in November 2024 as an open-source reasoning model aimed at competing with OpenAI\u2019s o1-preview.<\/p>\n\n\n\n At launch, the model was designed to enhance logical reasoning and planning by reviewing and refining its own responses during inference, a technique that made it particularly effective in math and coding tasks. <\/p>\n\n\n\n The initial version of QwQ featured 32 billion parameters and a 32,000-token context length, with Alibaba highlighting its ability to outperform o1-preview in mathematical benchmarks like AIME and MATH, as well as scientific reasoning tasks such as GPQA.<\/p>\n\n\n\n Despite its strengths, QwQ\u2019s early iterations struggled with programming benchmarks like LiveCodeBench, where OpenAI\u2019s models maintained an edge. Additionally, as with many emerging reasoning models, QwQ faced challenges such as language mixing and occasional circular reasoning loops. <\/p>\n\n\n\n However, Alibaba\u2019s decision to release the model under an Apache 2.0 license ensured that developers and enterprises could freely adapt and commercialize it, distinguishing it from proprietary alternatives like OpenAI\u2019s o1.<\/p>\n\n\n\n Since QwQ\u2019s initial release, the AI landscape has evolved rapidly. The limitations of traditional LLMs have become more apparent, with scaling laws yielding diminishing returns in performance improvements.<\/p>\n\n\n\n This shift has fueled interest in large reasoning models (LRMs) \u2014 a new category of AI systems that use inference-time reasoning and self-reflection to enhance accuracy. These include OpenAI\u2019s o3 series and the massively successful DeepSeek-R1 from rival Chinese lab DeepSeek, an offshoot of Hong Kong quantitative analysis firm High-Flyer Capital Management. <\/p>\n\n\n\n A new report from web traffic analytics and research firm SimilarWeb found that since the launch of R1 back in January 2024, DeepSeek has rocketed up the charts to become the most-visited AI model-providing website behind OpenAI.<\/p>\n\n\n\n QwQ-32B, Alibaba\u2019s latest iteration, builds on these advancements by integrating RL and structured self-questioning, positioning it as a serious competitor in the growing field of reasoning-focused AI.<\/p>\n\n\n\n Traditional instruction-tuned models often struggle with difficult reasoning tasks, but the Qwen Team\u2019s research suggests that RL can significantly improve a model\u2019s ability to solve complex problems.<\/p>\n\n\n\n QwQ-32B builds on this idea by implementing a multi-stage RL training approach to enhance mathematical reasoning, coding proficiency and general problem-solving.<\/p>\n\n\n\n The model has been benchmarked against leading alternatives such as DeepSeek-R1, o1-mini and DeepSeek-R1-Distilled-Qwen-32B, demonstrating competitive results despite having fewer parameters than some of these models.<\/p>\n\n\n\n For example, while DeepSeek-R1 operates with 671 billion parameters (with 37 billion activated), QwQ-32B achieves comparable performance with a much smaller footprint \u2014 typically requiring 24 GB of vRAM on a GPU (Nvidia\u2019s H100s have 80GB) compared to more than 1500 GB of vRAM for running the full DeepSeek R1 (16 Nvidia A100 GPUs) \u2014 highlighting the efficiency of Qwen\u2019s RL approach.<\/p>\n\n\n\n QwQ-32B follows a causal language model architecture and includes several optimizations:<\/p>\n\n\n\n The RL process for QwQ-32B was executed in two phases:<\/p>\n\n\n\n For enterprise leaders\u2014including CEOs, CTOs, IT leaders, team managers and AI application developers\u2014QwQ-32B represents a potential shift in how AI can support business decision-making and technical innovation.<\/p>\n\n\n\n With its RL-driven reasoning capabilities, the model can provide more accurate, structured and context-aware insights, making it valuable for use cases such as automated data analysis, strategic planning, software development and intelligent automation.<\/p>\n\n\n\n Companies looking to deploy AI solutions for complex problem-solving, coding assistance, financial modeling or customer service automation may find QwQ-32B\u2019s efficiency an attractive option. Additionally, its open-weight availability allows organizations to fine-tune and customize the model for domain-specific applications without proprietary restrictions, making it a flexible choice for enterprise AI strategies.<\/p>\n\n\n\n The fact that it comes from a Chinese e-commerce giant may raise some security and bias concerns for some non-Chinese users, especially when using the Qwen Chat interface. But as with DeepSeek-R1, the fact that the model is available on Hugging Face for download and offline usage and fine-tuning or retraining suggests that these can be overcome fairly easily. And it is a viable alternative to DeepSeek-R1.<\/p>\n\n\n\n The release of QwQ-32B has already gained attention from the AI research and development community, with several developers and industry professionals sharing their initial impressions on X (formerly Twitter):<\/p>\n\n\n\n QwQ-32B incorporates agentic capabilities, allowing it to dynamically adjust reasoning processes based on environmental feedback.<\/p>\n\n\n\n For optimal performance, Qwen Team recommends using the following inference settings:<\/p>\n\n\n\n The model supports deployment using vLLM, a high-throughput inference framework. However, current implementations of vLLM only support static YaRN scaling, which maintains a fixed scaling factor regardless of input length.<\/p>\n\n\n\n Qwen\u2019s team sees QwQ-32B as the first step in scaling RL to enhance reasoning capabilities. Looking ahead, the team plans to:<\/p>\n\n\n\n With QwQ-32B, Qwen Team is positioning RL as a key driver of the next generation of AI models, demonstrating that scaling can produce highly performant and effective reasoning systems.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>Quan-with-Questions was Alibaba\u2019s answer to OpenAI\u2019s original reasoning model o1<\/h2>\n\n\n\n
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Scaling up performance with multi-stage reinforcement learning<\/h2>\n\n\n\n
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What it means for enterprise decision-makers<\/h2>\n\n\n\n
Early reactions from AI power users and influencers<\/h2>\n\n\n\n
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Agentic capabilities<\/h2>\n\n\n\n
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Future developments<\/h2>\n\n\n\n
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