Reinforcement learning (RL) is rapidly evolving beyond token-based models, embracing physics-inspired scaling laws and verifiable rewards to tackle complex problems like the Erdos conjecture. In 2026, RL systems will leverage test-time computation and sparse reward strategies, bridging the gap between AI and truth. Insights from OpenAI’s Dan Roberts reveal how these advancements are reshaping coding, mathematics, and real-world applications. Discover the hidden forces driving AI’s next leap.