We’re excited to announce the availability of Qwen QwQ-32B on Chat-O! This powerful model, developed by Qwen, leverages Reinforcement Learning (RL) to achieve performance comparable to the much larger DeepSeek-R1 (671 billion parameters). As usual, the model is integrated on Chat-O on the same day that it was announced! The original announcement by Qwen can be seen here.
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Qwen’s research demonstrates the effectiveness of RL in enhancing large language models. QwQ-32B showcases this, achieving remarkable performance through RL applied to a robust foundation model. Integrated agent-related capabilities enable critical thinking, tool utilization, and adaptation based on environmental feedback.
As Qwen states:
Our research explores the scalability of Reinforcement Learning (RL) and its impact on enhancing the intelligence of large language models. We are excited to introduce QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated). This remarkable outcome underscores the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge. Furthermore, we have integrated agent-related capabilities into the reasoning model, enabling it to think critically while utilizing tools and adapting its reasoning based on environmental feedback. These advancements not only demonstrate the transformative potential of RL but also pave the way for further innovations in the pursuit of artificial general intelligence.
Try Qwen QwQ-32B today on Chat-O and experience the future of AI!