The Evolution of Reasoning in Small Language Models with Yejin Choi - #761

About this episode

Yejin Choi discusses her work on enhancing reasoning in small language models and the societal implications of AI.

Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can unlock stronger reasoning capabilities in smaller models. Yejin explains the risks of homogeneity in model outputs and mode collapse highlighted in her “Artificial Hivemind” paper, and its impacts on human creativity and knowledge. We also discuss her team's novel approaches, including reinforcement learning as a pre-training objective, where models are incentivized to “think” before predicting the next token, and "Prismatic Synthesis," a gradient-based method for generating diverse synthetic math data while filtering overrepresented examples. Additionally, we cover the societal implications of AI and the concept of pluralistic alignment—ensuring AI reflects the diverse norms and values…

People in this episode

Host: Sam Charrington

Guest: Yejin Choi

Topics covered

• small language models
• reasoning capabilities
• data generation
• reinforcement learning
• AI implications
• democratizing AI

Keywords

• language models
• reasoning
• synthetic data
• imitation learning
• reinforcement learning
• AI ethics
• pluralistic alignment

Mentioned in this episode

Organizations: Stanford University, Institute for Human-Centered AI

Books & works: Artificial Hivemind

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