arXiv Machine Learning
Aug 6
GFlowNet Training by Policy Gradients
arXiv:2408. 05885v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
Stable GFlowNets with TV Monitoring and Probabilistic Guarantees
arXiv:2605. 01729v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward.
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arXiv AI
Jun 16
Proximal Policy Optimization for Amortized Discrete Sampling
arXiv:2606. 15793v1 Announce Type: cross Abstract: This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework.
arXiv Machine Learning
Jun 5
Your GFlowNet Secretly Learns an Optimal Transport Plan
arXiv:2606. 06272v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph.
arXiv Machine Learning
Aug 5
Information-Geometric Forward Policy Training in GFlowNets
arXiv:2608. 03967v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects, requiring only an unnormalised target density specified through a reward.
arXiv Machine Learning
Jul 21
Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602. 21565v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial.
arXiv Machine Learning
Jun 16
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
arXiv:2606. 16073v1 Announce Type: new Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling.
arXiv AI
Jul 28
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
arXiv:2410. 02596v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms.
arXiv AI
Jul 15
Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models
arXiv:2607. 12171v1 Announce Type: cross Abstract: In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform denoising.
Hugging Face Trending Papers
Jul 13
Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models
In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform denoising. Although prior work shows that these parameterizations lead to different empirical behaviors, the mechanisms underlying their respective advantages remain to be underexplored, and how to combine them effectively is still unclear.
arXiv Machine Learning
Jul 1
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
arXiv:2605. 00553v3 Announce Type: replace Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety.
arXiv Machine Learning
Aug 7
IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games
arXiv:2608. 05422v1 Announce Type: new Abstract: While many algorithms blend reinforcement learning (RL) with counterfactual regret (CFR) methods to leverage tradeoffs in computational speed and performance, there are fewer investigations into generative sampling frameworks in game theoretic applications in incomplete information games.
arXiv Machine Learning
Jul 16
GFlowRL: Scaling Distribution-Matching RL to Large Language Models
arXiv:2607. 13394v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths by matching reward distributions rather than collapsing to dominant modes.