Reinforcement learning with verifiable rewards

Reasoning has become a defining capability of large language models, yet the learning mechanisms behind its emergence and generalization remain poorly understood. Yu Huang examines how chain-of-thought training on short problems enables length generalization, and how self-improvement can extend that ability further. The talk then analyses how the reasoning frontier evolves under reinforcement learning with verifiable rewards, revealing an implicit curriculum that emerges from mixed-difficulty training and showing how the spectrum of problem difficulty shapes learning dynamics. The talk is one

…inference-time computation and reinforcement learning with verifiable rewards. The discussion connects these methods with self-consistency, model architectures, efficient attention and long contexts, while examining limits in continual learning and the reliability of agent workflows.