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Improving GFlowNets with Monte Carlo Tree Search
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Generative Flow Networks (GFlowNets) treat sampling from distributions over compositional discrete spaces as a sequential decision-making problem, training a stochastic policy to construct objects step by step. Recent studies have revealed strong connections between GFlowNets and entropy-regularized reinforcement learning. Building on these insights, we propose to enhance planning capabilities of GFlowNets by applying Monte Carlo Tree Search (MCTS). Specifically, we show how the MENTS algorithm (Xiao et al., 2019) can be adapted for GFlowNets and used during both training and inference. Our experiments demonstrate that this approach improves the sample efficiency of GFlowNet training and the generation fidelity of pre-trained GFlowNet models.
Forward citations
Cited by 2 Pith papers
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Revisiting Non-Acyclic GFlowNets in Discrete Environments
In cyclic discrete environments, GFlowNet flows are expected visit counts, and training a non-acyclic GFlowNet with the smallest expected trajectory length is equivalent to minimizing total flow.
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Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Diffusion Tree Sampling is a Monte Carlo tree search over denoising trajectories that propagates terminal rewards backward to sample from reward-aligned distributions, showing up to 10x compute savings on tested benchmarks.
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