AI Research
Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks
Solving mode collapse in synthetic data generation through GFlowNets for superior AI reasoning
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 d...
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ArXiv CS.AI
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ArXiv CS.AI
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Generative Flow Networks offer a breakthrough in overcoming mode collapse by effectively exploring the vast distribution of expert-level conversational strategies. This methodology enables the creation of high-fidelity synthetic datasets that are essential for training more robust and versatile AI agents.
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