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The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sample objects proportionally to an unnormalized reward function.
Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E · 1953
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
Earlier work this paper cites.
Learning to predict by the methods of temporal differences
Sutton, R. S · 1988
Earlier work this paper cites.
Reinforcement learning and dynamic programming
Barto, A. G · 1995
Earlier work this paper cites.
Algorithms for sequential decision-making
Littman, M. L · 1996
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 1998
Earlier work this paper cites.
An introduction to mcmc for machine learning
Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I · 2003
Earlier work this paper cites.
Learning in markov random fields using tempered transitions
Salakhutdinov, R. R · 2009
Earlier work this paper cites.
Viennarna package 2.0
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., and Hofacker, I. L · 2011
Earlier work this paper cites.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Taming the noise in reinforcement learning via soft updates
Fox, R., Pakman, A., and Tishby, N · 2015
Earlier work this paper cites.
Empirical evaluation of rectified activations in convolutional network
Xu, B., Wang, N., Chen, T., and Li, M · 2015
Earlier work this paper cites.
A unified view of entropy-regularized markov decision processes
Neu, G., Jonsson, A., and Gómez, V · 2017
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Earlier work this paper cites.
Deep reinforcement learning and the deadly triad
Van Hasselt, H., Doron, Y., Strub, F., Hessel, M., Sonnerat, N., and Modayil, J · 2018
Earlier work this paper cites.
A theory of regularized markov decision processes
Geist, M., Scherrer, B., and Pietquin, O · 2019
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Revisiting the softmax bellman operator: New benefits and new perspective
Song, Z., Parr, R., and Carin, L · 2019
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Reinforcement learning with dynamic boltzmann softmax updates
Pan, L., Cai, Q., Meng, Q., Chen, W., and Huang, L · 2020
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Munchausen reinforcement learning
Vieillard, N., Pietquin, O., and Geist, M · 2020
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Molecular mechanics-driven graph neural network with multiplex graph for molecular structures
Zhang, S., Liu, Y., and Xie, L · 2020
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Flow network based generative models for non-iterative diverse candidate generation
Local search gflownets
Kim, M., Yun, T., Bengio, E., Zhang, D., Bengio, Y., Ahn, S., and Park, J · 2023
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Bridging rl theory and practice with the effective horizon
Laidlaw, C., Russell, S. J., and Dragan, A · 2023
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Li, Y., Luo, S., Shao, Y., and Hao, J · 2023
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Learning gflownets from partial episodes for improved convergence and stability
Madan, K., Rector-Brooks, J., Korablyov, M., Bengio, E., Jain, M., Nica, A. C., Bosc, T., Bengio, Y., and Malkin, N · 2023
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Gflownets and variational inference
Malkin, N., Lahlou, S., Deleu, T., Ji, X., Hu, E. J., Everett, K. E., Zhang, D., and Bengio, Y · 2023
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Towards understanding and improving gflownet training
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Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y · 2021
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Regularized softmax deep multi-agent q-learning
Pan, L., Rashid, T., Peng, B., Huang, L., and Whiteson, S · 2021
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Bayesian structure learning with generative flow networks
Deleu, T., Góis, A., Emezue, C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y · 2022
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Amortized variational inference: Towards the mathematical foundation and review
Ganguly, A., Jain, S., and Watchareeruetai, U · 2022
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Biological sequence design with gflownets
Jain, M., Bengio, E., Hernandez-Garcia, A., Rector-Brooks, J., Dossou, B. F., Ekbote, C. A., Fu, J., Zhang, T., Kilgour, M., Zhang, D., et al · 2022
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Trajectory balance: Improved credit assignment in gflownets
Malkin, N., Jain, M., Bengio, E., Sun, C., and Bengio, Y · 2022
Cited alongside, same era.
Generative augmented flow networks
Pan, L., Zhang, D., Courville, A., Huang, L., and Bengio, Y · 2022
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Shen, M. W., Bengio, E., Hajiramezanali, E., Loukas, A., Cho, K., and Biancalani, T · 2023
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Discrete probabilistic inference as control in multi-path environments
Deleu, T., Nouri, P., Malkin, N., Precup, D., and Bengio, Y · 2024
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Amortizing intractable inference in large language models
Hu, E. J., Jain, M., Elmoznino, E., Kaddar, Y., Lajoie, G., Bengio, Y., and Malkin, N · 2024
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Learning energy decompositions for partial inference of gflownets
Jang, H., Kim, M., and Ahn, S · 2024
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Qgfn: Controllable greediness with action values
Lau, E., Lu, S. Z., Pan, L., Precup, D., and Bengio, E · 2024
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Learning diverse attacks on large language models for robust red-teaming and safety tuning
Lee, S., Kim, M., Cherif, L., Dobre, D., Lee, J., Hwang, S. J., Kawaguchi, K., Gidel, G., Bengio, Y., Malkin, N., et al · 2024
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Maximum entropy gflownets with soft q-learning
Mohammadpour, S., Bengio, E., Frejinger, E., and Bacon, P.-L · 2024
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Gflownet training by policy gradients
Niu, P., Wu, S., Fan, M., and Qian, X · 2024
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Pre-training and fine-tuning generative flow networks
Pan, L., Jain, M., Madan, K., and Bengio, Y · 2024
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Generative flow networks as entropy-regularized rl
Tiapkin, D., Morozov, N., Naumov, A., and Vetrov, D. P · 2024
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Let the flows tell: Solving graph combinatorial problems with gflownets
Zhang, D., Dai, H., Malkin, N., Courville, A. C., Bengio, Y., and Pan, L · 2024
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Learning to sample effective and diverse prompts for text-to-image generation
Yun, T., Zhang, D., Park, J., and Pan, L · 2025
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