mlsanityData to 7 Oct 2026

Robust Scheduling with GFlowNets

arXiv 2302.05446 ↗First posted 17 Jan 2023cs.AI cs.LG cs.PLcited by 67

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Abstract (arXiv)

Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimize proxy metrics, which are fast to evaluate but can lead to bad schedules when tested on the target hardware. In this work, we propose a new approach to scheduling by sampling proportionally to the proxy metric using a novel GFlowNet method. We introduce a technique to control the trade-off between diversity and goodness of the proposed schedules at inference time and demonstrate empirically that the pure optimization baselines can lead to subpar performance with respect to our approach when tested on a target model. Furthermore, we show that conditioning the GFlowNet on the computation graph enables generalization to unseen scheduling problems for both synthetic and real-world compiler datasets.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

22 of 43 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  6. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  7. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  8. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 244
  9. 2020How Neural Networks Extrapolate: From Feedforward to Graph Neural Networkscited by 218
  10. 2020pymoo: Multi-objective Optimization in Pythoncited by 196
  11. 2022Biological Sequence Design with GFlowNetscited by 172
  12. 2018Understanding the impact of entropy on policy optimizationcited by 171
10 more
  1. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  2. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 139
  3. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 133
  4. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 113
  5. 2018Observe and Look Further: Achieving Consistent Performance on Ataricited by 42
  6. 2020Transferable Graph Optimizers for ML Compilerscited by 29
  7. 2019Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learningcited by 26
  8. 2019Reinforced Genetic Algorithm Learning for Optimizing Computation Graphscited by 17
  9. 2022Neural Topological Ordering for Computation Graphscited by 12
  10. 2020Optimizing Memory Placement using Evolutionary Graph Reinforcement Learningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 19; 2024: 21; 2025: 11; 2026: 16.

67 papers.

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  1. 2023A theory of continuous generative flow networkscited by 98
  2. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  3. 2024Learning diverse attacks on large language models for robust red-teaming and safety tuningcited by 54
  4. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
  5. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  6. 2024Improved off-policy training of diffusion samplerscited by 47
  7. 2023Local Search GFlowNetscited by 45
  8. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  9. 2023Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetscited by 40
  10. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 40
  11. 2025FlowRL: Matching Reward Distributions for LLM Reasoningcited by 38
  12. 2023Stochastic Generative Flow Networkscited by 32
38 more
  1. 2023Thompson sampling for improved exploration in GFlowNetscited by 31
  2. 2024Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNetscited by 30
  3. 2024Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examplescited by 29
  4. 2023Distributional GFlowNets with Quantile Flowscited by 28
  5. 2024Improving GFlowNets for Text-to-Image Diffusion Alignmentcited by 27
  6. 2025Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Trainingcited by 27
  7. 2023Learning to Scale Logits for Temperature-Conditional GFlowNetscited by 26
  8. 2023PhyloGFN: Phylogenetic inference with generative flow networkscited by 20
  9. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  10. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 16
  11. 2025Learning to Sample Effective and Diverse Prompts for Text-to-Image Generationcited by 16
  12. 2025GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networkscited by 15
  13. 2023Order-Preserving GFlowNetscited by 13
  14. 2024Pessimistic Backward Policy for GFlowNetscited by 13
  15. 2024Can a Bayesian Oracle Prevent Harm from an Agent?cited by 12
  16. 2025Adversarial Generative Flow Network for Solving Vehicle Routing Problemscited by 10
  17. 2023torchgfn: A PyTorch GFlowNet librarycited by 9
  18. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  19. 2024Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNetscited by 8
  20. 2025Revisiting Non-Acyclic GFlowNets in Discrete Environmentscited by 8
  21. 2023Expected flow networks in stochastic environments and two-player zero-sum gamescited by 7
  22. 2024Baking Symmetry into GFlowNetscited by 7
  23. 2024ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learningcited by 7
  24. 2024MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNetscited by 6
  25. 2024On Divergence Measures for Training GFlowNetscited by 6
  26. 2025Adaptive Destruction Processes for Diffusion Samplerscited by 6
  27. 2026SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestrationcited by 6
  28. 2024Random Policy Evaluation Uncovers Policies of Generative Flow Networkscited by 5
  29. 2025Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNetscited by 4
  30. 2023GenCO: Generating Diverse Designs with Combinatorial Constraintscited by 3
  31. 2024Embarrassingly Parallel GFlowNetscited by 3
  32. 2025Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generationcited by 3
  33. 2026Avoid What You Know: Divergent Trajectory Balance for GFlowNetscited by 3
  34. 2023BatchGFN: Generative Flow Networks for Batch Active Learningcited by 2
  35. 2025Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learningcited by 2
  36. 2024Bifurcated Generative Flow Networkscited by 1
  37. 2025A Generative Neural Annealer for Black-Box Combinatorial Optimizationcited by 1
  38. 2026GFlowRL: Scaling Distribution-Matching RL to Large Language Modelscited by 1

50 most cited of 67 All 67 (JSON)