QwQ-32B: Scaling Reinforcement Learning for Enhanced Model Performance
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QwQ-32B: Embracing the Power of Reinforcement Learning | Qwen.
Scaling Reinforcement Learning for Enhanced Model Performance
Reinforcement Learning (RL) is emerging as a powerful tool to augment model performance beyond traditional pretraining and post-training methods. Recent studies indicate that RL can significantly improve a model's reasoning capabilities, as demonstrated by models achieving state-of-the-art performance through integrating data and multi-stage training.
QwQ-32B: A New Benchmark
QwQ-32B, a model with 32 billion parameters, showcases the effectiveness of RL. It achieves performance levels comparable to models with significantly larger parameter counts. This highlights the potential of RL when applied to robust foundation models pretrained on extensive datasets. The model also integrates agent-related capabilities, enabling it to reason critically while utilizing tools and adapting its reasoning based on environmental feedback.
Training Methodology
The development of QwQ-32B involved a reinforcement learning approach driven by outcome-based rewards. This involved scaling RL for math and coding tasks. Instead of relying on traditional reward models, the developers used an accuracy verifier for math problems and a code execution server to assess the correctness of generated code. A subsequent stage of RL was then added for general capabilities, trained with rewards from a general reward model and rule-based verifiers. This additional stage was found to increase performance in areas like instruction following and alignment with human preference, without a significant performance drop in math and coding.
Applications and Examples
QwQ-32B can be implemented using Hugging Face Transformers and Alibaba Cloud DashScope API.
Example question:
"How many r's are in the word reinforcement learning?"Example API usage:
# If the environment variable is not configured, replace with your API Key: api_key="sk-xxx"
# How to get an API Key：https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key
"Which is larger, 9.9 or 9.11?"
# Uncomment the following line to return token usage in the last chunk
# "include_usage": True
# If chunk.choices is empty, print usage
The Future of RL in Language Models
This development marks a step toward scaling Reinforcement Learning (RL) to enhance reasoning capabilities. As research progresses, the combination of robust foundation models with RL, powered by increased computational resources, could propel the field closer to Artificial General Intelligence (AGI). Further exploration of integrating agents with RL may also unlock greater intelligence through long-horizon reasoning.