Summary of Improving Gflownets with Monte Carlo Tree Search, by Nikita Morozov et al.
Improving GFlowNets with Monte Carlo Tree Search
by Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to enhance the planning capabilities of Generative Flow Networks (GFlowNets) by applying Monte Carlo Tree Search (MCTS). The authors build upon recent studies that reveal strong connections between GFlowNets and entropy-regularized reinforcement learning. They show how the MENTS algorithm can be adapted for GFlowNets, using it during both training and inference. The experiments demonstrate improved sample efficiency of GFlowNet training and generation fidelity of pre-trained GFlowNet models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GFlowNets are a type of generative model that creates objects step by step. Researchers have found connections between GFlowNets and a type of learning called entropy-regularized reinforcement learning. To make GFlowNets better at planning, the authors suggest using an algorithm called Monte Carlo Tree Search (MCTS). They show how to adapt this algorithm for GFlowNets, and use it during both training and making predictions. The results show that this approach makes GFlowNet training faster and improves the quality of the objects generated. |
Keywords
» Artificial intelligence » Generative model » Inference » Reinforcement learning
