Why hasn't longer-horizon training slowed AI progress? (www.seangoedecke.com)

Dwarkesh Patel has initiated a challenge to explore why AI progress continues to accelerate, despite the assumption that longer-horizon training and more complex tasks would slow development. His inquiry focuses on reinforcement learning and models' increasing demands for floating-point operations (FLOPs). Traditionally, as AI models become more sophisticated, the training process is expected to take longer and require more computational resources. However, current trends, highlighted by the METR horizon-length graph, show models demonstrating rapid advancements in capability, raising questions about the efficiency and effectiveness of resource utilization in AI training. The AI/ML community finds significance in this conversation as it challenges conventional metrics of progress and intelligence. Patel suggests that the perceived acceleration in AI development may be attributed to improvements in training efficiency rather than a linear increase in raw computational power. Moreover, intelligence alone is not the sole factor determining AI capabilities; traits such as persistence, working memory, and adaptability play critical roles. This nuanced understanding invites further exploration into the complexities of AI training dynamics and signals that despite challenges, there are opportunities for innovation and breakthroughs without solely relying on brute-force computation.