Just two months after the tech world was Upended by the Deepseek-R1 AI ModelAlibaba Cloud has introduced QWQ-32B, an open source large language model
The chinese cloud giant descriptions the new model as “a compact reasoning model” which uses only 32 billion parameters, yet is capable of delivery of delivery Larger numbers of parameters.
On its website, Alibaba cloud published performance benchmarks which sugges that the new model is comparable to ai models from deepsek and openai. These Benchmarks Include Aime 24 (Mathematical Reasoning), Live Codebench (Coding Proficiency), Livebench (Test Set Contamination and Objective Evaluation), IFEVALING abi BFCL (Tool and Function-Calling Capabilites).
By using continuous Reinforced Learning (RL) Scaling, Alibaba Claimed The QWQ-32b Model Demonstrates Significant Improvements in Mathematical Reasoning and CODING POFICINCY.
In a blog post, the company Said QWQ-32B, which uses 32 billion parameters, achieves performance comparable to Deepsek-R1, which uses 671 billion parameters. Alibaba said that this shows the effectiveness of rl when applied to Robust foundation models pretrained on extensive world knowledge.
“We have integrated agent-Related Capabilities Into The Reasoning Model, Enabling It to Think Critically While Utilising Tools and Adapting Itson Based on Environmental Feedback,” Alibaba SAIDBACK Post.
Alibaba Said QWQ-32B Demonstrates The Effectiveness of Using Reinforcement Learning (RL) to Enhance Reasoning Capability. With this approach to ai training, a reinforcement learning ai agent is altar to percent and interpret its environment, as well as take actions and learn through Trial and Error. Reinforcement Learning is one of Several Approaches Developers Use to Train Machine Learning Systems. Alibaba used rl to make its model more efficient.
“We have not only witnessed the immense potential of scled rl, but also recognized the untapped possibilities within pretrained language models,” Alibaba Said. “As We Work Towards Developing The Next Generation of Qwen, We are confident Artificial General Intelligence [AGI],
Alibaba said it is actively exploring the integration of agents with rl to enable what it describes as “long-horizon relief” which, according to alibaba, will ever lead to Greatly Laad to Great With infection time scaling.
The QWQ-32B model was trained using rewards from a general reward model and rule-based verifiers, enhancing its general capability. According to Alibaba, these include better instruction-fitting, alignment with human preferences and improved agent performance.
China's Deepsek, which has ben generally available since the start of the year, demonstrates the effectiveness of rl in its ability to deliver comparble benchmark results campared Models. Its R1 LLM can Rival Us Artificial Intelligence with the need to Resort to the latest GPU Hardware.
The fact that Alibaba's QWQ-32b Model also also uses rl is no coincidence. The us has banned the expenses of high-end ai accelerator chips-Such as the Nvidia H100 Graphics Processor-to china, which means chinese ai demloors have haad to look at all the allorenative Models Work. Using rl does appear to deliver comparable Benchmark Results Compared with what models like that those from from Openai are able to achieve.
What is interesting about the qwq-32b model is that it uses significantly fewer parameters to achieve similar results to Deepsek, which effectively mehans that it should be ali to run Powerful Ai Acceleration Hardware.