The newly released QWQ 32B model by Alibaba is a remarkable achievement in the field of large language models. Despite having only 32 billion parameters, compared to the 671 billion parameters of the Deep Seek R1 model, QWQ 32B delivers comparable performance across various benchmarks.

According to the provided information, QWQ 32B achieves a score of 79.5% on the Amy 2024 benchmark, which is very close to the 79.8% score of Deep Seek R1. Additionally, it outperforms Deep Seek R1 on the BFCL benchmark by 6 points.

The key to QWQ 32B's success lies in the use of reinforcement learning (RL) techniques. The model was trained using a scaled computational approach, with outcome-based rewards for math and coding tasks. This allowed the model to develop strong reasoning and problem-solving capabilities, which are crucial for achieving high performance on various benchmarks.

Furthermore, the model's small size and efficient architecture make it possible to run it on personal computers, a significant advantage over the resource-intensive Deep Seek R1. This accessibility opens up new possibilities for developers and researchers to experiment and integrate QWQ 32B into their projects.

While the model's performance on the GPT QA Diamond benchmark is slightly lower than Deep Seek R1 and Gemini 2.0 Flash, its strong showing on the Amy 2024 math benchmark is a testament to its capabilities. The report also notes that the model's performance may be further improved by applying techniques like chain-of-thought prompting, which can help streamline the model's thinking process.

Overall, the QWQ 32B model represents a significant step forward in the development of efficient and high-performing large language models. Its combination of strong performance, small size, and open-source availability make it a compelling option for a wide range of applications.

The model developers used a reinforcement learning (RL) approach to train the QWQ 32B model, specifically focusing on math and coding tasks. They started with a cold start checkpoint and implemented RL with a scaling approach driven by outcome-based rewards.

The key aspects of their approach include:

1. Reinforcement Learning with Verifiable Rewards: Instead of relying on traditional reward models, they utilized an accuracy verifier for math problems to ensure the correctness of final solutions. For coding tasks, they used a code execution server to assess whether the generated code successfully passed predefined test cases.

2. Two-Stage RL Training: After the initial stage of RL training on math and coding tasks, they added another stage of RL training for general capabilities. This hybrid approach allowed the model to first excel at math and coding, and then generalize its capabilities through further RL training.

3. Scaling Computational Resources: The developers emphasized that combining stronger foundation models with RL, powered by scaled computational resources, will push them closer to achieving artificial general intelligence (AGI).

4. Exploration of Agent-Based RL: The team is actively exploring the integration of agents with reinforcement learning to enable long-horizon reasoning and unlock greater intelligence with inference time scaling.

Overall, this reinforcement learning approach, combined with a strong foundation model and scaled computational resources, has resulted in the impressive QWQ 32B model, which demonstrates comparable performance to the much larger Deep Seek R1 model while being significantly more efficient and accessible.

Alibaba's researchers took a multi-stage approach to expand the capabilities of the QWQ 32B model beyond just math and coding tasks. After the initial stage of reinforcement learning focused on math and coding, they added another stage to increase the model's performance on more general capabilities.

The researchers found that this additional stage of reinforcement learning, with a small number of training steps, could boost the model's instruction following, alignment with human preferences, and overall agent performance, without significant drops in its math and coding abilities.

This hybrid approach, combining a strong foundation model with reinforcement learning, is seen by the researchers as a promising path towards achieving artificial general intelligence (AGI). They express confidence that by continuing to improve foundation models and scaling up the computational resources for reinforcement learning, they can make progress in realizing more general and capable AI systems.

Additionally, the team is actively exploring the integration of agents with reinforcement learning, aiming to unlock even greater intelligence through inference time scaling. This suggests a focus on developing agents that can leverage the model's reasoning capabilities for long-horizon tasks and decision-making.

Overall, the QWQ 32B model and Alibaba's approach demonstrate the potential of combining powerful foundation models with reinforcement learning to expand the boundaries of what is possible with current AI systems.

Alibaba's recently released QWQ 32B model is a significant step towards achieving Artificial General Intelligence (AGI). The model, part of the Quen series, is comparable in performance to the much larger Deep Seek R1 model, but with a fraction of the parameters, making it easily runnable on personal computers.

The key to QWQ 32B's success is the use of reinforcement learning (RL) techniques, similar to those used by OpenAI for their GPT-3 and GPT-4 models. By applying RL with verifiable rewards, the model is able to develop strong reasoning and problem-solving capabilities, particularly in areas like math and coding.

The authors of the model are confident that combining powerful foundation models with RL, powered by scaled computational resources, will bring us closer to achieving AGI. They are also actively exploring the integration of agents with RL to enable long-horizon reasoning and unlock greater intelligence.

The impressive performance and efficiency of QWQ 32B, with its ability to run at 450 tokens per second, demonstrate the potential of this approach. While the model may not outperform Deep Seek R1 on all benchmarks, its smaller size and open-source nature make it an exciting development in the quest for AGI.

The blog post highlights the impressive performance of the new QWQ 32B model from Alibaba, which is part of the Quen series of models. This model is comparable to the much larger DeepSEEK R1 model in terms of capabilities, but with significantly fewer parameters (32B vs. 671B).

The key advantages of the QWQ 32B model are:

• Smaller Size: The 32B parameter count means it can be easily run on a personal computer, unlike the massive 671B DeepSEEK R1.
• Comparable Results: Benchmarks show the QWQ 32B model performs nearly as well as DeepSEEK R1 on tasks like GPT-QA Diamond and AMY 2024.
• Blazing Fast Inference: When hosted on Grok GRQ, the QWQ 32B model can achieve an incredible 450 tokens per second, allowing for extremely rapid thinking and iteration.

The blog post delves into the model's development, explaining how the authors used reinforcement learning with verifiable rewards on math and coding tasks to instill strong reasoning capabilities. They then further fine-tuned the model for more general capabilities.

While the model does have a smaller 132k context window compared to some larger models, the incredible inference speed and open-source nature of the QWQ 32B make it an exciting development in the field of large language models.

The blog post provides a detailed analysis of the benchmark performances of the new QWQ 32B model released by Alibaba. Here are the key points:

• The QWQ 32B model is comparable to the much larger DeepSEEK R1 model (671 billion parameters) in terms of performance, despite having only 32 billion parameters.
• On the Amy 2024 benchmark, the QWQ 32B model scored 78%, matching the claims made by the Alibaba team and outperforming DeepSEEK R1.
• However, on the GPT-QA Diamond scientific reasoning benchmark, the QWQ 32B model scored 59.5%, which is materially behind DeepSEEK R1's score of 71% and just behind Gemini 2.0 Flash's 62%.
• The blog post notes that the QWQ 32B model has 20 times fewer parameters than DeepSEEK R1's 671 billion, and even fewer than DeepSEEK's 37 billion active parameters.
• While the QWQ 32B model was trained and released in bfloat16 format, DeepSEEK R1 was trained and released natively in float32. This means that on hardware with native fp8 support, like Nvidia's H100, DeepSEEK R1 may actually use less effective compute per forward pass.
• Overall, the blog post concludes that the QWQ 32B model is still a very impressive model, with its small size and high efficiency, but it also highlights some areas where it may not perform as well as the larger DeepSEEK R1 model.

The release of qwq 32b by Alibaba is an exciting development in the world of large language models. This 32-billion parameter model is comparable in performance to the much larger 671-billion parameter Deep Seek R1, while being significantly more efficient and able to run on regular computer hardware.

The key to qwq 32b's success is the use of reinforcement learning, which has been used to imbue the model with strong reasoning and problem-solving capabilities. By starting with a solid foundation model and then applying reinforcement learning with verifiable rewards for tasks like math and coding, the researchers were able to create a highly capable thinking model.

Furthermore, the model's impressive inference speed of 450 tokens per second, as demonstrated on the Grok platform, suggests that it has significant potential for real-world applications that require fast and efficient language processing.

While the model may not outperform Deep Seek R1 on all benchmarks, its combination of strong performance, small size, and open-source availability make it a highly compelling option for developers and researchers looking to work with advanced language models. The authors' ambition to continue pushing the boundaries of artificial general intelligence (AGI) with the integration of reinforcement learning and agents is also an exciting prospect for the future.

Overall, the release of qwq 32b represents an important step forward in the development of efficient and capable language models, and it will be interesting to see how the model is adopted and built upon by the wider AI community.