Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks

Excerpt

arXiv:2609.38359v1 Announce Type: cross Abstract: High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in conversations. Prompting LLMs directly or conditioning them on end use scenarios yields low diversity data that collapses onto dominant modes. We propose a method to generate diverse high quality synthetic data using Generative Flow Networks (GFlowNets). We show that training GFlowNets to generate