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

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 latent conversation structure using a Gaussian-mixture density over key interaction features (e.g., confusion-episode dynamics, scaffolding–directive balance) enables sampling expert strategies in proportion to their prevalence in the training data. Across two structurally distinct domains, tutoring and emotional-support dialogues, our GFlow-based synthetic data generation approach offers a better balance of fidelity, mode-coverage and authenticity than reinforcement-learning and end-to-end LLM baselines, without copying training data. Evaluated on three downstream outcome prediction tasks, classifiers trained on synthetic GFlowNet-generated conversations provide a stronger training signal than competitive synthesis baselines.