Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay

Reasoning and reinforcement learning (RL) represent the current frontier of AI development, shifting from imitation-based training to on-policy methods where models refine their own trajectories through self-generated rewards. This paradigm shift enabled the development of end-to-end models capable of achieving IMO Gold, demonstrating that general-purpose architectures can outperform specialized symbolic systems. AI now functions as a powerful research multiplier, with models increasingly automating complex coding and data analysis tasks, effectively acting as "passive auras" that enhance human productivity. While scaling remains central, the field is increasingly focused on data efficiency and the search for novel learning algorithms that maximize compute per token. As research labs consolidate, the ability to demonstrate "research taste" and execute independent, high-impact experiments remains a critical differentiator for emerging talent in an increasingly competitive landscape.