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RE: LeoThread 2025-03-07 04:21

in LeoFinance7 months ago

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:

  1. 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.