Event Details

Speaker: Zixiang Chen, UCLA

Conference Rm Location: ISI-MDR #689 in-person attendance will be permitted for USC/ISI faculty, staff, students only. Open to the public virtually via Zoom

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Hosts: Jon May and Justin Cho

Abstract-Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this talk, I will introduce our newest fine-tuning method, Self-Play Fine-Tuning (SPIN), which improves LLMs without the need for additional human-annotated data. SPIN utilizes a self-play mechanism, where the LLM enhances its capabilities by generating its own training data through interactions with instances of itself. Specifically, the LLM generates its own training data from its previous iterations, refining its policy by discerning these self-generated responses from those obtained from human-annotated data. As a result, SPIN unlocks the full potential of human-annotated data for SFT. Our empirical results show that SPIN can improve the LLM’s performance across a variety of benchmarks and even outperform models trained through direct preference optimization (DPO) supplemented with extra GPT-4 preference data. Additionally, I will outline the theoretical guarantees of our method. For more details and access to our codes, visit our GitHub repository (https://github.com/uclaml/SPIN).

Speaker Bio

Zixiang Chen is currently a Ph.D. student in computer science at the Department of Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Quanquan Gu. He obtained his bachelor’s degree in mathematics from Tsinghua University. He is broadly interested in the theory and applications of deep learning, optimization, and control, with a focus on generative models, representation learning, and multi-agent reinforcement learning. Recently, he has been utilizing AI to enhance scientific discovery in the domain of public health. He was a visiting graduate student in the theory of reinforcement learning program at the Simons Institute for the Theory of Computing.

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