mlsanityData to 7 Oct 2026

Evolution Guided Generative Flow Networks

arXiv 2402.02186 ↗First posted 3 Feb 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

Generative Flow Networks (GFlowNets) are a family of probabilistic generative models that learn to sample compositional objects proportional to their rewards. One big challenge of GFlowNets is training them effectively when dealing with long time horizons and sparse rewards. To address this, we propose Evolution guided generative flow networks (EGFN), a simple but powerful augmentation to the GFlowNets training using Evolutionary algorithms (EA). Our method can work on top of any GFlowNets training objective, by training a set of agent parameters using EA, storing the resulting trajectories in the prioritized replay buffer, and training the GFlowNets agent using the stored trajectories. We present a thorough investigation over a wide range of toy and real-world benchmark tasks showing the effectiveness of our method in handling long trajectories and sparse rewards. We release the code at http://github.com/zarifikram/egfn.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 63 references that are in the map. First posted on arXiv 3 Feb 2024.

Builds on

40 of 63 references.

References · 40

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  11. 2022Biological Sequence Design with GFlowNetscited by 172
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28 more
  1. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 129
  2. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  3. 2020Effective Diversity in Population Based Reinforcement Learningcited by 116
  4. 2022Multi-Objective GFlowNetscited by 100
  5. 2023A theory of continuous generative flow networkscited by 98
  6. 2022Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimizationcited by 96
  7. 2020A Graph to Graphs Framework for Retrosynthesis Predictioncited by 85
  8. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 83
  9. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 78
  10. 2023Towards Understanding and Improving GFlowNet Trainingcited by 74
  11. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  12. 2018CEM-RL: Combining evolutionary and gradient-based methods for policy searchcited by 53
  13. 2022Generative Augmented Flow Networkscited by 48
  14. 2020Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structurescited by 44
  15. 2018GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithmscited by 40
  16. 2019Maximum Entropy-Regularized Multi-Goal Reinforcement Learningcited by 39
  17. 2023Protein Discovery with Discrete Walk-Jump Samplingcited by 38
  18. 2023Stochastic Generative Flow Networkscited by 32
  19. 2023Thompson sampling for improved exploration in GFlowNetscited by 31
  20. 2023Distributional GFlowNets with Quantile Flowscited by 28
  21. 2023Learning to Scale Logits for Temperature-Conditional GFlowNetscited by 26
  22. 2022GFlowOut: Dropout with Generative Flow Networkscited by 22
  23. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 16
  24. 2014Neuroevolution in Games: State of the Art and Open Challengescited by 15
  25. 2018PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptationcited by 14
  26. 2020An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Searchcited by 12
  27. 2020Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)cited by 5
  28. 2023Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countriescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Trainingcited by 2
  2. 2026Exploring Multiple High-Scoring Subspaces in Generative Flow Networkscited by 0
  3. 2026Stable GFlowNets with TV Monitoring and Probabilistic Guaranteescited by 0