Cover Image

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dang, Meihua, Han, Jiaqi, Xu, Minkai, Xu, Kai, Srivastava, Akash, Ermon, Stefano
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

Divergence Minimization Preference Optimization for Diffusion Model Alignment
by: Li, Binxu, et al.
Published: (2025)

Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
by: Han, Jiaqi, et al.
Published: (2025)

Geometric Trajectory Diffusion Models
by: Han, Jiaqi, et al.
Published: (2024)

RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
by: Chen, Tianlang, et al.
Published: (2025)

CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
by: Han, Jiaqi, et al.
Published: (2025)

$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization
by: Han, Jiaqi, et al.
Published: (2024)

Personalized Preference Fine-tuning of Diffusion Models
by: Dang, Meihua, et al.
Published: (2025)

TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
by: Shi, Juntong, et al.
Published: (2024)

Energy-Based Diffusion Language Models for Text Generation
by: Xu, Minkai, et al.
Published: (2024)

TFG: Unified Training-Free Guidance for Diffusion Models
by: Ye, Haotian, et al.
Published: (2024)

Scaling Probabilistic Circuits via Monarch Matrices
by: Zhang, Honghua, et al.
Published: (2025)

Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
by: Ou, Jingyang, et al.
Published: (2025)

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models
by: Hudovernik, Valter, et al.
Published: (2025)

SNLP: Layer-Parallel Inference via Structured Newton Corrections
by: Han, Ligong, et al.
Published: (2026)

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
by: Puri, Isha, et al.
Published: (2025)

MUDiff: Unified Diffusion for Complete Molecule Generation
by: Hua, Chenqing, et al.
Published: (2023)

MADiff: Offline Multi-agent Learning with Diffusion Models
by: Zhu, Zhengbang, et al.
Published: (2023)

Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations
by: Selvam, Nikil Roashan, et al.
Published: (2024)

Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
by: Yang, Ling, et al.
Published: (2024)

Contextualized Diffusion Models for Text-Guided Image and Video Generation
by: Yang, Ling, et al.
Published: (2024)

Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
by: Wang, Austin, et al.
Published: (2026)

Few-Step Diffusion Language Models via Trajectory Self-Distillation
by: Zhang, Tunyu, et al.
Published: (2026)

Equivariant Graph Neural Operator for Modeling 3D Dynamics
by: Xu, Minkai, et al.
Published: (2024)

Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
by: Iyengar, Aniketh, et al.
Published: (2025)

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
by: Gu, Siyi, et al.
Published: (2024)

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion
by: Kazdan, Joshua, et al.
Published: (2024)

Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
by: Giannone, Giorgio, et al.
Published: (2025)

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics
by: Iyengar, Aniketh, et al.
Published: (2026)

GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters
by: Zhao, Wanjia, et al.
Published: (2025)

Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
by: Kang, Taegu, et al.
Published: (2026)

Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
by: Shen, Yangyi, et al.
Published: (2026)

Data Unlearning in Diffusion Models
by: Alberti, Silas, et al.
Published: (2025)

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
by: Lou, Aaron, et al.
Published: (2023)

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
by: Manvi, Rohin, et al.
Published: (2024)

Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
by: Han, Jiaqi, et al.
Published: (2026)

Data-regularized Reinforcement Learning for Diffusion Models at Scale
by: Ye, Haotian, et al.
Published: (2025)

Test-Time Scaling of Diffusion Models via Noise Trajectory Search
by: Ramesh, Vignav, et al.
Published: (2025)

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
by: Cundy, Chris, et al.
Published: (2023)

Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding
by: Guo, Gabe, et al.
Published: (2025)

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
by: Guo, Gabe, et al.
Published: (2026)