[Submitted on 29 Sep 2026]
Title:Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks
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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 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.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2609.38359 [cs.AI]
(or arXiv:2609.38359v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.38359
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sumit Asthana [view email] [v1] Tue, 29 Sep 2026 18:21:50 UTC (1,761 KB)
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