Part 4/9:
The foundation of AZR lies in previous advancements in Reinforcement Learning through Verifiable Rewards (RLVR). This learning model relies on outcome-based feedback, enabling AI to learn from vast datasets devoid of human supervision. For instance, in mathematical tasks where correct answers can be objectively verified, the AI can autonomously learn without needing a human to affirm its success or failure.
However, traditional RLVR still requires carefully curated datasets, limiting AI advancement. The challenges surrounding this high-quality human-generated content raise concerns about future scalability. As AI intelligence evolves, the human-curated tasks may not suffice for providing the learning potential needed by increasingly intelligent systems.