A Taxonomy for Next-Generation Reasoning

Explore a comprehensive conference talk that presents a four-trait taxonomy for developing next-generation AI reasoning capabilities, focusing on skills, calibration, strategy, and abstraction as essential components for creating reliable long-horizon AI agents. Learn about the current limitations of AI models that excel at immediate tasks but struggle with medium-term planning and execution. Discover how strategy and abstraction traits specifically enable the development of more sophisticated agentic behaviors that can handle complex, multi-step problems. Examine the role of reinforcement learning with verifiable rewards in advancing AI capabilities and understand the challenges facing current AI systems in real-world applications. Gain insights into the research methodologies and computational approaches needed to train more effective reasoning models. Understand the anticipated shift in resource allocation from pre-training to post-training techniques as the field evolves toward more capable AI systems. The presentation concludes with predictions about how these agentic behaviors will serve as the foundation for continued scaling in reinforcement learning and the eventual compute parity between post-training and pre-training methods.