Intel / article

QwQ-32B: Embracing the Power of Reinforcement Learning

Source: Qwen Team
Author: Qwen Team
Date: Published Mar 5, 2025

Terms in this piece · Glossary

pretraining — The first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.

Why it matters

QwQ-32B delivers reasoning performance competitive with far larger models by scaling reinforcement learning, making strong deep-thinking capability feasible to run and at a 32B footprint — relevant if you need frontier-level reasoning without frontier-scale hardware.

Key quotes

“Scaling Reinforcement Learning (RL) has the potential to enhance model performance beyond conventional pretraining and post-training methods.”

“For instance, DeepSeek R1 has achieved state-of-the-art performance by integrating cold-start data and multi-stage training, enabling deep thinking and complex reasoning.”

Read the source ↗qwenlm.github.io