The Evolution of Reasoning in Small Language Models

Yejin Choi examines how data diversity, synthetic examples, imitation learning and reinforcement learning can strengthen reasoning in small language models. The conversation discusses output homogeneity, Prismatic Synthesis, training models to reason before predicting tokens, and pluralistic alignment that represents a broader range of human values.