mlsanityData to 14 Oct 2026

A theory of continuous generative flow networks

arXiv 2301.12594 ↗First posted 30 Jan 2023cs.LG stat.MLcited by 101

Not on the map. Why?

Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

23 of 45 references.

References · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  5. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  6. 2021GFlowNet Foundationscited by 290
  7. 2022Torsional Diffusion for Molecular Conformer Generationcited by 263
  8. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 251
  9. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 233
  10. 2021Path Integral Sampler: a stochastic control approach for samplingcited by 181
  11. 2022Biological Sequence Design with GFlowNetscited by 174
  12. 2020Stochastic Normalizing Flowscited by 157
11 more
  1. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  2. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 132
  3. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 114
  4. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  5. 2022GFlowNets and variational inferencecited by 106
  6. 2023Robust Scheduling with GFlowNetscited by 69
  7. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  8. 2022Generative Augmented Flow Networkscited by 50
  9. 2022A Variational Perspective on Generative Flow Networkscited by 39
  10. 2021Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpointcited by 32
  11. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 26; 2024: 34; 2025: 25; 2026: 16.

101 papers.

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  1. 2024Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Controlcited by 143
  2. 2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densitiescited by 116
  3. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 85
  4. 2025Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matchingcited by 85
  5. 2024Amortizing intractable inference in diffusion models for vision, language, and controlcited by 72
  6. 2023Generative Flow Networks as Entropy-Regularized RLcited by 64
  7. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  8. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  9. 2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Samplingcited by 56
  10. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  11. 2024Improved off-policy training of diffusion samplerscited by 48
  12. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 44
38 more
  1. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  2. 2023Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetscited by 40
  3. 2024Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Predictioncited by 32
  4. 2024Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNetscited by 32
  5. 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 31
  6. 2023Distributional GFlowNets with Quantile Flowscited by 28
  7. 2025No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplerscited by 28
  8. 2023Compositional Sculpting of Iterative Generative Processescited by 27
  9. 2023Learning to Scale Logits for Temperature-Conditional GFlowNetscited by 27
  10. 2024Improving GFlowNets for Text-to-Image Diffusion Alignmentcited by 27
  11. 2024Adaptive teachers for amortized samplerscited by 23
  12. 2023PhyloGFN: Phylogenetic inference with generative flow networkscited by 22
  13. 2024Reward-Directed Score-Based Diffusion Models via q-Learningcited by 19
  14. 2025From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingcited by 19
  15. 2024QGFN: Controllable Greediness with Action Valuescited by 18
  16. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  17. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 17
  18. 2024Sampling from Energy-based Policies using Diffusioncited by 17
  19. 2025Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative modelscited by 17
  20. 2025Learning to Sample Effective and Diverse Prompts for Text-to-Image Generationcited by 17
  21. 2024SymbolicAI: A framework for logic-based approaches combining generative models and solverscited by 16
  22. 2023Multi-Fidelity Active Learning with GFlowNetscited by 15
  23. 2025Generative Models in Decision Making: A Surveycited by 15
  24. 2025Offline Model-Based Optimization: Comprehensive Reviewcited by 15
  25. 2023Generative Flow Networks: a Markov Chain Perspectivecited by 14
  26. 2023Towards equilibrium molecular conformation generation with GFlowNetscited by 14
  27. 2023Improving Gradient-guided Nested Sampling for Posterior Inferencecited by 14
  28. 2024Pessimistic Backward Policy for GFlowNetscited by 14
  29. 2024Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challengescited by 14
  30. 2023Entropy-based Training Methods for Scalable Neural Implicit Samplercited by 13
  31. 2024Iterated Energy-based Flow Matching for Sampling from Boltzmann Densitiescited by 13
  32. 2023A Theory of Non-Acyclic Generative Flow Networkscited by 12
  33. 2023torchgfn: A PyTorch GFlowNet librarycited by 11
  34. 2024Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networkscited by 11
  35. 2025Revisiting Non-Acyclic GFlowNets in Discrete Environmentscited by 10
  36. 2025On scalable and efficient training of diffusion samplerscited by 10
  37. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  38. 2024PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimationcited by 8

50 most cited of 101 All 101 (JSON)