ICLR 2026PastGenerative modelsTheory
ICLR 2026 2nd Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy
ICLR 2026 DeLTa Workshop
- Submission deadline
- Feb 9, 2026, 12:59 UTCOpenReview-synced 2026-02-09 12:59 UTC (as of 2026-06-23) — extensions on OpenReview are applied automatically; verify on the website.
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (133)
Fetched from OpenReview (v2) on 2026-06-10.
$\mathbf{R^3}$-Adapter: Progressive Residual Refinement and Representational Alignment for Personalized Image Generation
$W_K, W_V$ is Probably All You Need: On the Necessity of the Query, Key, and Value Weight Triplet in Self-Attention Transformers
A Complete Decomposition of Stochastic Differential Equations
A Diffusive Classification Loss for Learning Energy-based Generative Models
A Geometric Perspective on Recursive Synthetic Training
A Graph-Theoretical View of Space Folding via the Motzkin–Straus Framework
A Unified Density Operator View of Flow Control and Merging
Adapting Noise to Data by Quantile Learning
AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization
An Efficient Test-Time Scaling Approach for Image Generation
An Equivariance Toolbox for Learning Dynamics
ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling
AnimalBooth: Multimodal Feature Enhancement for Animal Subject Personalization
AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
Attention Projection Mixing with Exogenous Anchors
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
B-DENSE: Branching For Dense Ensemble Network Supervision Effeciency
Balancing Symmetry and Efficiency in Graph Flow Matching
BézierFlow: Learning Bézier Stochastic Interpolant Schedulers for Few-Step Generation
BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers
BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
CATS: Inference-aligned SFT for Diffusion LLMs via Context-sensitivity Aware Trajectory Sampling
CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly
Curriculum Sampling: A Two-Phase Curriculum for Efficient Training of Flow Matching
Data-Aware Random Feature Kernel for Transformers
Decoding Large Language Diffusion Models with Foreseeing Movement
Decoupled Diffusion Solver for Inverse Problems on Function Spaces
DELTA: Robustly Training Diffusion Models with Weak Annotations
Demystifying Transition Matching: When and Why It Can Beat Flow Matching
Designing Continuous Conditioning for GANs from WAE Latent Structure
Dichotomous Diffusion Policy Optimization
Diffusion Models with Double Guidance
Diffusion Policy Optimization without Drifting Apart
Diffusion Schrödinger Bridge Matching: When Resampling Fails
DiffusionShield: A Watermarking Approach to Safeguarding Video Integrity Against Stable Diffusion
Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
Discrete Adjoint Schrödinger Bridge Sampler
Discrete Bridges for Mutual Information Estimation
Discrete Diffusion Samplers and Bridges: Off-Policy Algorithms and Applications in Latent Spaces
Discrete Meanflow Training Curriuculum
Discriminative Multimodal Preference Models as Guidance for Personalized Image Generation
Dynamic Mixture-of-Experts for Visual Autoregressive Model
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Elucidating Guidance in Variance Exploding Diffusion Models: Fast Convergence and Better Diversity
Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
Evaluating the Role of Great Pre-trained Diffusion Models in Few-shot Phase: Warm-up and Acceleration
Expert-Data Alignment Governs Generation Quality in Decentralized Diffusion Models
Exposing Diversity Bias in Deep Generative Models: Statistical Origins and Correction of Diversity Error
Flow Matching based Conditional Independence Tests and Causal Structure Learning
Flow Matching in the Low-Noise Regime: Pathologies and a Contrastive Remedy
FMMI: Flow Matching Mutual Information Estimation
From Compression to Expression: A Layerwise Analysis of In-Context Learning
Generative Hints
Generative Model via Quantile Assignment
Gradual Fine-Tuning for Flow Matching Models
Grokking of Diffusion Models: Case Study on Modular Addition
GUIDE: Guided Initialization and Distillation of Embeddings
Heterogeneous Low-Bandwidth Pre-Training of LLMs
Higher-order grammar representations for molecular generation and learning
Information-Geometric Optimal Control for Diffusion Models: Unified Framework via Fisher-Rao Geodesics
Informative Data Reweighting for Image Classification
Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
Inverse-distilled Diffusion Language Models
Latent Process Generator Matching
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
Learning Unmasking Policies for Diffusion Language Models
Log-density Hessian estimation without the curse of dimensionality via denoising score matching
Low-Pass Flow Matching
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
Maximum Entropy under Carre du Champ Constraints
Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
MixFlow: Mixed Source Distributions Improve Rectified Flows
On Closed-Form Couplings
On the "Induction Bias" in Sequence Models
On the Lipschitz Regularity of Optimal Discriminators
On the Memorization of Consistency Distillation for Diffusion Models
On the Use of Schrödinger Bridges for Tabular Data Generation
One LR Doesn’t Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs
One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
Optimal Learning-Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay
Overclocking Electrostatic Generative Models
Paired Wasserstein Autoencoders for Conditional Sampling
PairFlow: Closed-Form Source-Target Coupling for Few-Step Generation in Discrete Flow Models
Path Invariance and the Robustness of Flow Matching: Beyond Architectural and Data Perturbations
pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows
Performance Limits of Score-Based Generative Models via Stochastic Thermodynamics
Permutation-Symmetrized Diffusion for Unconditional Molecular Generation
Pre-training Large Language Models with Dynamic Precision: Low-Cost Computation with High-Fidelity Performance
Principled Randomized Exploration of Gradient Subspaces for Efficient LLM Training
Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
Query Lower Bounds for Diffusion Sampling
Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference
Rethinking Reparameterization of Stochastic Processes in Generative Modeling
Reward-Guided Discrete Diffusion via Clean-Sample Markov Chain
Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
Robust Graph Diffusion Model
Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
Scalable Sampling via Generalized Fixed-Point Diffusion Matching
Schrödinger bridge problem via empirical risk minimization
Score-Guided Proximal Projection: A Unified Geometric Framework for Rectified Flow Editing
Search or Accelerate: Confidence-Switched Position Beam Search for Diffusion Language Models
SHAPE: SCHEDULE HESSIAN ADAPTIVE PARAMETER ESTIMATION FOR SMOOTHER DIFFUSION OPTIMIZATION
SingLoRA: Low Rank Adaptation Using a Single Matrix
Skip To The Good Part: Representation Structure & Inference-Time Layer Skipping in Diffusion vs Autoregressive LLM
Sliding Critical Band in RoPE-based Length Extrapolation
Spectral Condition for $\mu$P under Width–Depth Scaling
Steering diffusion models with quadratic rewards: a fine-grained analysis
Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Stochastic Few-step Models
Strong Reward Only: Pareto-Guided Multi-Reward Optimization
Structured image representation learning for flow-matching models
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Time Dependent Loss Reweighting for Flow Matching and Diffusion Models is Theoretically Justified
Time-Correlated Video Bridge Matching
Tokenize, Diffuse, Decode: A Generative Approach to Neighborhood Discovery on Graphs
Training Flow Matching: The Role of Weighting and Parameterization
Training-Free Length Discovery for Diffusion Language Model Infilling
Understanding Deterministic Diffusion through Reverse Transition Kernels
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Unlocking the Duality between Flow and Field Matching
Video Unlearning via Low-Rank Refusal Vector
What Flow-Matching Brings To TD-Learning
What Lies Beneath the Curve? Scaling Laws in the Presence of Exact Posteriors
When Does Sparsity Mitigate the Curse of Depth in LLMs
When Does Stein Beat Antithetic Sampling? Distribution Complexity in Discrete Gradient Estimation
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
WiSP-OSch: Solver Within-Step Parallelism and Order Scheduling for Diffusion Sampling
Zero-Flow Encoders