Reservoir Engineering and Simulation Methods

Últimos artículos

• DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

  Zhengming Yu, Junkun Yuan, Haotian Yang, Gordon Guocheng Qian, Yizhi Wang, Angtian Wang, Yiding Yang, Bo Liu, Xin Li, Wenping Wang, Chongyang Ma · 2 de octubre de 2026

  Distribution Matching Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it must keep an auxiliary diffusion model fitted to the student's evolving distribution at extra memory and computation cost. We introduce DMAD, Distribut…

• Finetuning with Sampling: SFT Learns Better Than You Think

  Aayush Karan, Sitan Chen, Yilun Du · 2 de octubre de 2026

  Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and reinforcement learning (RL) to this end. Conventional wisdom dictates that RL enables strong generalization on new tasks without losing existing capabil…

• Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training

  Hanna Malet, Gabriel Turinici · 2 de octubre de 2026

  Model collapse arises when generative models are trained on synthetic data produced by earlier models. The phenomenon has attracted considerable attention because of its societal and technical implications. However, previous studies have reached seemingly contradictory conclusions: replacing real da…

• Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps

  Yeongmin Kim, Arnaud Doucet, Andrew Campbell, Valentin De Bortoli, Thomas Mensink, David Ruhe · 2 de octubre de 2026

  We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We theoretically demonstrate that the coupling between Gumbel noise and one-hot token sequences induced by an autoregressive model yi…

• Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment

  Jinho Chang, Jong Chul Ye · 2 de octubre de 2026

  Recent advances in distillation and flow-map models have enabled deterministic one- or few-step generation for high-quality data, facilitating a new branch of reward alignment approaches that directly optimize the initial noise from a Gaussian distribution. However, most existing initial-noise optim…

• Cool the Sampler, Not the Learner: Sampling Temperature Moves the Staleness Cliff of Importance-Corrected GRPO

  Taiheng Pan · 1 de octubre de 2026

  Production RL for language models lets the sampler fall behind the learner and repairs the resulting mismatch with a truncated importance weight. We ask how long the sampler can go without a refresh under that correction, and find a cliff: on Qwen2.5-Math-1.5B and GSM8K, importance-corrected GRPO re…

• AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD

  Xigui Li, Yuanye Zhou, Feiyang Xiao, Xin Guo, Chen Jiang, Tan Pan, Xingmeng Zhang, Cenyu Liu, Zeyun Miao, Xiansheng Wang, Qimeng Wang, Yichi Zhang, Wenbo Zhang, Hongwei Zhang, Ruoxi Jiang, Fengping Zhu, Limei Han, Chensen Lin, Yuan Cheng · 1 de octubre de 2026

  Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape editing can expand limited geometry collections, but whether its variants improve prediction on unseen geometries, and how to allocate them across sources, req…

• Fenchel Tilting: Weighted Correction for Efficient Finetuning of Generative Models

  Maksim Bobrin, Maksim Zhdanov, Dmitry Dylov · 1 de octubre de 2026

  Adapting a pretrained generative model to an arbitrary preference expressed as a utility function underlies reward alignment, guided design, and constraint satisfaction, enabling diverse applications. Existing fine-tuning methods trade off generality against computational cost: they either restrict …

• GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales

  Zhenxing Zhang, Jiayan Teng, Wenxu Wu, Zhuoyi Yang, Jiazheng Xu, Wendi Zheng, Jie Tang, Dan Guo, Meng Wang · 1 de octubre de 2026

  On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merging expert models into a single model. Existing diffusion OPD, however, mostly focus on the latter, with teachers and students sharing the same backbo…

• Towards Universal Wasserstein Barycenters through Flow Matching

  Eduardo Fernandes Montesuma · 1 de octubre de 2026

  Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning. Under the Wasserstein metric, these are called \emph{Wasserstein barycenters}. While most approaches compute barycenters for a fixed weight vector, approximating the whol…

• MeanFlowAdvantage: Stable Reward Fine-Tuning for Few-Step Average-Velocity Generators

  Haocheng Tang, Tianchi Xie, Xingqiao Lin · 30 de septiembre de 2026

  MeanFlow enables efficient few-step generation by predicting interval-average velocities, but this representation creates a mismatch for reward fine-tuning: existing advantage-based objectives are typically defined on instantaneous velocities or equivalent $x_0$-space predictions, whereas inference …

• Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?

  Wenze Lin, Jiyuan Long, Jiale Zhao, Shenzhi Wang, Xitai Jiang, Ce Luo, Rui Lan, Qianli Ma, Fukang Wen, Hui Wu, Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Gao Huang · 29 de septiembre de 2026

  Since the advent of knowledge distillation, KL divergence has been the standard loss in distillation. Recently, on-policy distillation (OPD) has emerged as an efficient post-training paradigm for LLMs. As a distillation method, OPD naturally inherits KL divergence as its standard loss. However, in t…

• Activation Flow: Manufacturing Activations for Steering

  Hong Kiat Tan, Linh Le, David Williams-King · 29 de septiembre de 2026

  Difference-in-means steering requires activations recorded while a model shows the desired behavior, which a sandbagging model withholds by deliberately underperforming. We introduce Activation Flow (ActFlow), which manufactures these activations from $k$ correct labels without fine-tuning. ActFlow …

• A Flow Matching Framework for Neural Representational Dissimilarity

  Zeyuan Ye, Xue-Xin Wei · 28 de septiembre de 2026

  Neural representational dissimilarity quantifies differences between neural response distributions, and is essential for comparing neural codes across stimuli, brain areas, tasks, and models. Commonly used distance metrics involve different assumptions and are estimated with separate methods. Here, …

• WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps

  Abbas Mammadov, Jerry Y. Huang, Justin Lin, Partha Kaushik, Sheel Shah, Kartik Nair, Yee Whye Teh, Nicholas M. Boffi · 24 de septiembre de 2026

  Reward fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples. Existing methods typically formulate this problem as sampling from a reward-tilted distribution, the solution to a KL-regularized reward-maximization problem. Here, …

• Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles

  Junyi Ye · 23 de septiembre de 2026

  When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this influence without retraining. We benchmark temporal unlearning with 3,200 paired references trained on all data and oracles retrained without the re…

• Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow

  Tung Sum Thomas Kwok, Yidong Ouyang, Yingjia Wan, Ying Nian Wu, Zhijiang Guo, Oscar Leong · 23 de septiembre de 2026

  Uniform discrete flow permits repeated updates at every generation position. While continued revision supports correction of wrong tokens, it also exposes correct intermediate predictions to later errors. An experiment on Sudoku puzzles shows that 9.4% of generated cells are correct at an intermedia…

• Terminal Shrinkage Averaging Reveals a Schedule-Estimator Interaction in LLM Pretraining

  Adam Ousherovitch, Yixin Wang · 23 de septiembre de 2026

  Large language model (LLM) pretraining conventionally returns the raw final iterate. This couples two design choices: the learning-rate schedule that generates the parameter trajectory and the estimator that constructs the deployed model (e.g. the raw final iterate or a checkpoint average). A schedu…

• When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedules

  Subhadip Mitra · 22 de septiembre de 2026

  Training under non-stationary but predictable compute availability (satellites under eclipse, duty-cycled edge devices, power-capped datacenters) is often framed as needing specialized, availability-aware optimizers. We test that premise. We release OrbitTrace, a benchmark of 50 physics-grounded ava…

• Whitening Inverts the Hierarchy: What the Norm of a Whitened Embedding Measures

  Mohammed Ahnouch, Lotfi Elaachak · 22 de septiembre de 2026

  Whitening a foundation-model embedding and using its squared norm as a training-free likelihood surrogate is motivated by the observation that whitened coordinates often appear approximately standard normal. We show that this observation follows from the projection central limit theorem and therefor…

• Classifier-Free Guidance in Flow Matching: Non-Autonomous Potentials, Overshoot, and Posterior-Mean Control

  Jishen Peng, Zheng Ma · 22 de septiembre de 2026

  Classifier-free guidance (CFG) improves conditional generation in Flow Matching, but strong guidance can distort the generated distribution and reduce diversity. We provide a geometric account of this behavior by viewing Flow Matching as a time-varying gradient flow and characterizing how CFG reshap…

• $\lambda$-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Budgeted Resource

  Yufeng Wang, Parivesh Priye, Meeshawn Marathe, Ramit Pahwa · 21 de septiembre de 2026

  Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matching models by treating the denoising sampler as a stochastic policy that can be optimized from reward feedback. Training in this setting is unstable i…

• A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios

  Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger, Ahmed H. Elsheikh · 18 de septiembre de 2026

  The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow th…

• FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants

  Tianao Li, Xinhui Qian, Emma Alexander · 18 de septiembre de 2026

  Flow matching has emerged as the state-of-the-art generative model and has been used for plug-and-play (PnP) priors to solve inverse problems in computational imaging. However, existing flow-based inverse solvers assume linear forward models and/or make simplifying approximations in posterior sampli…

• How to Guide Your Language Flow

  Rohit Dilip, Tianrong Chen, Yuyang Wang, David Van Valen, Joshua Susskind, Miguel Angel Bautista · 18 de septiembre de 2026

  We introduce a new method to guide flow matching models. Our approach, which we call probe guidance, uses the frozen internal states of an existing diffusion model to construct a guidance signal. This works using a similar principle as autoguidance, but eliminates the need for an additional forward …