Fenchel Tilt Flow Control (FTFC) is a new method for fine‑tuning pretrained generative models to arbitrary preference functions. It decouples utility optimization from model fitting by first learning reward and density‑ratio weights on pretrained samples, then freezing these weights to adjust a diffusion or flow model in a single importance‑weighted stage. The approach supports general f‑divergence penalties, achieves exact duality for concave utilities, and demonstrates up to 20× efficiency gains while outperforming baselines on image and molecule generation tasks.
By Maksim Bobrin, Maksim Zhdanov, Dmitry Dylov
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.
By Yordan Raykov, Rodrigo Veiga
PMosFM introduces a preconditioned manifold matching framework that enables one‑step physics‑constrained generation by encoding constraints in a manifold decoder. The method learns transport in intrinsic coordinates, eliminating the need for residual losses or trajectory unrolling, and employs a geometric preconditioner and covariance transform to improve conditioning. Experiments demonstrate reduced training and sampling time compared to multi‑step baselines while maintaining comparable physical and distributional fidelity.
By Zhangyong Liang, Haibin Ling
The paper introduces Cumulative Marginal Matching (CMM), a new objective for training few‑step diffusion and flow‑matching models. CMM treats flow‑matching as a sequence of intermediate marginal distributions and directly optimizes the cumulative Wasserstein‑2 discrepancy between the base and few‑step models across the entire trajectory. It derives a tractable surrogate for this objective, shows that Shortcut‑CSL is a first‑order approximation of CMM, and reports improved generation quality on standard benchmarks under the same training budget.
By Paribesh Regmi, Rui Li
The paper introduces a generalized score matching objective for parameter estimation on convex subsets of ρ^d, derived from Minimum Probability Flow learning. It shows that this objective is a proper local scoring rule of second order, ensuring recovery of the true density when minimized, and proves convexity and consistency for exponential family models under standard conditions. Experiments demonstrate the method’s effectiveness on constrained domains where the partition function is intractable, including a generative modeling use‑case.
By Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar Seelamantula
arXiv:2609.38920v1 Announce Type: new
Abstract: Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives...
By Yuan Lu, Esha Singh, Yi-An Ma, Yusu Wang
arXiv:2603. 27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space.
By Andrea Fraschini, Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
The paper introduces Normalizing Flow Maps (NFM), a new class of exactly invertible pushforward maps that extend normalizing flows and flow maps by learning the integrator of an underlying dynamical system while enabling efficient exact likelihood evaluation. NFMs incorporate two consistency axes—pointwise matching for β1-based regression losses and distributional matching for aligning pushforward distributions with a forward process—allowing scalable training. Experiments on CIFAR-10, ImageNet-64, and Boltzmann Generators demonstrate that NFMs achieve state‑of‑the‑art sample quality among parameter‑matched normalizing flows and outperform prior normalizing flows on peptide benchmarks.
By Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng, Danyal Rehman, Zander W. Blasingame, Nicholas M. Boffi, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose
arXiv:2607. 22004v1 Announce Type: new Abstract: Energy natural gradient descent (ENGD) aligns parameter updates with the curvature of an underlying function-space energy, but existing formulations assume an unconstrained Euclidean parameter domain.
By Zhangyong Liang, Huanhuan Gao
The paper presents a method that combines flow‑matching models with energy‑based modeling to explicitly construct scalar energy functions for physical fields. These energies are derived from a matching regression objective on a linear Gaussian interpolation, avoiding variational approximations or extra MCMC steps, and can be used for energy‑corrected data generation, out‑of‑distribution detection, and posterior sampling in inverse problems. The approach enables general MCMC samplers that reduce PDE residuals and spectral distance, and it demonstrates that combining data‑driven and physics‑based energies improves OOD detection accuracy.
By Yixuan Sun, Anirban Samaddar, Sandeep Madireddy
arXiv:2606. 19876v1 Announce Type: new Abstract: The score matching problem is a central training objective in modern generative modeling, diffusion models, fitting unnormalized statistical models, and inverse problems.
By Alexander Tyurin
arXiv:2610.09310v1 Announce Type: cross
Abstract: A central question in data-driven inverse problems is how to construct a regularizer that adapts to the geometry of the data distribution. Recent wor...
By Carson Newman, Oscar Leong