Cover Image

Direct Distributional Optimization for Provable Alignment of Diffusion Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kawata, Ryotaro, Oko, Kazusato, Nitanda, Atsushi, Suzuki, Taiji
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
by: Oko, Kazusato, et al.
Published: (2024)

Pretrained transformer efficiently learns low-dimensional target functions in-context
by: Oko, Kazusato, et al.
Published: (2024)

Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality
by: Kawata, Ryotaro, et al.
Published: (2026)

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
by: Kim, Juno, et al.
Published: (2023)

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
by: Lee, Jason D., et al.
Published: (2024)

Flow matching achieves almost minimax optimal convergence
by: Fukumizu, Kenji, et al.
Published: (2024)

When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars
by: Higuchi, Rei, et al.
Published: (2025)

Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
by: Kawata, Ryotaro, et al.
Published: (2025)

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
by: Chen, Zonghao, et al.
Published: (2025)

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
by: Fu, Guoji, et al.
Published: (2026)

Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training
by: Bu, Dake, et al.
Published: (2025)

Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
by: Bu, Dake, et al.
Published: (2024)

Provable In-Context Vector Arithmetic via Retrieving Task Concepts
by: Bu, Dake, et al.
Published: (2025)

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis
by: Higuchi, Rei, et al.
Published: (2026)

Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble
by: Nitanda, Atsushi, et al.
Published: (2025)

Koopman-based generalization bound: New aspect for full-rank weights
by: Hashimoto, Yuka, et al.
Published: (2023)

State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
by: Nishikawa, Naoki, et al.
Published: (2024)

DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models
by: Bu, Dake, et al.
Published: (2026)

Alternating Diffusion for Proximal Sampling with Zeroth Order Queries
by: Takagi, Hirohane, et al.
Published: (2026)

Improved Particle Approximation Error for Mean Field Neural Networks
by: Nitanda, Atsushi
Published: (2024)

A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI
by: Oko, Kazusato, et al.
Published: (2025)

Transformers Provably Solve Parity Efficiently with Chain of Thought
by: Kim, Juno, et al.
Published: (2024)

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
by: Wakayama, Tomoya, et al.
Published: (2025)

Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
by: Higuchi, Rei, et al.
Published: (2025)

From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
by: Kawata, Ryotaro, et al.
Published: (2025)

Uniform convergence of the smooth calibration error and its relationship with functional gradient
by: Futami, Futoshi, et al.
Published: (2025)

How Does Preconditioning Guide Feature Learning in Deep Neural Networks?
by: Yoshida, Kotaro, et al.
Published: (2025)

Post-Training as Reweighting: A Stochastic View of Reasoning Trajectories in Language Models
by: Bu, Dake, et al.
Published: (2025)

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
by: Huang, Wei, et al.
Published: (2026)

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
by: Kim, Juno, et al.
Published: (2025)

Statistical Analysis of the Sinkhorn Iterations for Two-Sample Schrödinger Bridge Estimation
by: Maeda, Ibuki, et al.
Published: (2025)

Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes
by: Bossens, David M., et al.
Published: (2025)

Test time training enhances in-context learning of nonlinear functions
by: Kuwataka, Kento, et al.
Published: (2025)

Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
by: Kim, Juno, et al.
Published: (2024)

Deep Two-Way Matrix Reordering for Relational Data Analysis
by: Watanabe, Chihiro, et al.
Published: (2021)

The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge
by: Awano, Ryoya, et al.
Published: (2026)

Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
by: Takakura, Shokichi, et al.
Published: (2024)

AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network
by: Watanabe, Chihiro, et al.
Published: (2021)

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
by: Takakura, Shokichi, et al.
Published: (2023)

Why is parameter averaging beneficial in SGD? An objective smoothing perspective
by: Nitanda, Atsushi, et al.
Published: (2023)