Derives loss-to-TV bounds providing probabilistic guarantees for GFlowNets and introduces Stable GFlowNets algorithm for improved training stability and distributional fidelity.
International Conference on Learning Representations , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Unsupervised behavioral mode discovery combined with mutual information rewards enables RL fine-tuning of multimodal generative policies that achieves higher success rates without losing action diversity.
citing papers explorer
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Stable GFlowNets with Probabilistic Guarantees
Derives loss-to-TV bounds providing probabilistic guarantees for GFlowNets and introduces Stable GFlowNets algorithm for improved training stability and distributional fidelity.
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Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
Unsupervised behavioral mode discovery combined with mutual information rewards enables RL fine-tuning of multimodal generative policies that achieves higher success rates without losing action diversity.