Part 3/8:
At the heart of qwq 32b's design is the implementation of reinforcement learning (RL) strategies, a technique similarly leveraged by OpenAI in its earlier models. By applying RL to a smaller foundational model, the researchers managed to cultivate a thinking model capable of critical assessment and effective tool usage, making it well-suited for diverse applications.
The development process involved two significant RL stages:
- Outcome-Based Reinforcement Learning: Initially, qwq 32b was trained using an outcome-based reward system specifically tailored for math and coding tasks. This approach allowed for substantial verifiability of the model's performance and accuracy.