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cs.LG updates on arXiv.org

Recursive Flow Matching SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation Near-Optimal Regret in Adversarial Kernel Bandits Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models Balancing Plasticity and Stability with Fast and Slow Successor Features Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling Max-Window Scale Estimation for Near-Lossless HiF8 W8A8 Quantization-Aware Training PILOT: A Data-Free Continual Learning Approach for Real-Time Semantic Segmentation via Boundary Guidance HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models Linear and Neural Dueling Bandits with Delayed Feedback Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models Variational Inference for Evidential Deep Learning CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works TrackRef3D: Multi-View Consistent Track-then-Label for Open-World Referring Segmentation in 3D Gaussian Splatting A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice Towards Controllable Image Generation through Representation-Conditioned Diffusion Models When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models Is an Image Also Worth 16x16=256 Superpixels? 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Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
Jaewoo Lee, · 2026-05-27 · via cs.LG updates on arXiv.org

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Abstract:Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solver, or a particular model family. We introduce FAV, Few-step Generative Models Alignment via Sample-based Variational Inference, a general alignment framework that requires only sample access to the generator and the reference distribution. We cast alignment as sampling from a reward-tilted distribution anchored to a reference distribution. We leverage Stein Variational Gradient Descent as a sample-based variational inference scheme and amortize its particle updates into the generator parameters via fixed-point regression. We evaluate FAV on two domains: robotics manipulation and image generator alignment. On generative policy alignment for robotic manipulation, FAV outperforms prevailing policy extraction baselines across 56 offline and 30 offline-to-online RL tasks. For image generator alignment, FAV fine-tunes diverse few-step backbones, including GAN, drifting model, consistency models, and flow maps, scaling from ImageNet-$256$ to 1024$^2$ text-to-image synthesis. Code is available at this https URL.
Comments: Under review
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.26552 [cs.LG]
  (or arXiv:2605.26552v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.26552

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jaewoo Lee [view email]
[v1] Tue, 26 May 2026 05:02:49 UTC (18,419 KB)