Copertina

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kim, Yeongmin, Shin, Donghyeok, Na, Byeonghu, Park, Minsang, Kim, Richard Lee, Moon, Il-Chul
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Diffusion Rejection Sampling
di: Na, Byeonghu, et al.
Pubblicazione: (2024)

Distillation of Large Language Models via Concrete Score Matching
di: Kim, Yeongmin, et al.
Pubblicazione: (2025)

AMiD: Knowledge Distillation for LLMs with $α$-mixture Assistant Distribution
di: Shin, Donghyeok, et al.
Pubblicazione: (2025)

Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
di: Kim, Yeongmin, et al.
Pubblicazione: (2024)

Preference Optimization by Estimating the Ratio of the Data Distribution
di: Kim, Yeongmin, et al.
Pubblicazione: (2025)

Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models
di: Na, Byeonghu, et al.
Pubblicazione: (2025)

Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
di: Na, Byeonghu, et al.
Pubblicazione: (2025)

Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
di: Shin, Jiwoo, et al.
Pubblicazione: (2025)

Reward-based Input Construction for Cross-document Relation Extraction
di: Na, Byeonghu, et al.
Pubblicazione: (2024)

Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
di: Na, Byeonghu, et al.
Pubblicazione: (2026)

Training Unbiased Diffusion Models From Biased Dataset
di: Kim, Yeongmin, et al.
Pubblicazione: (2024)

Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
di: Lee, Sanghyun, et al.
Pubblicazione: (2025)

Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy
di: Kim, Minsang, et al.
Pubblicazione: (2024)

Label-Noise Robust Diffusion Models
di: Na, Byeonghu, et al.
Pubblicazione: (2024)

Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
di: Lee, Sanghyun, et al.
Pubblicazione: (2025)

Distilling Dataset into Neural Field
di: Shin, Donghyeok, et al.
Pubblicazione: (2025)

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
di: Bae, HeeSun, et al.
Pubblicazione: (2024)

Adaptive Guidance for Retrieval-Augmented Masked Diffusion Models
di: Kim, Jaemin, et al.
Pubblicazione: (2026)

Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction
di: Kim, Minsang, et al.
Pubblicazione: (2024)

Verifier-free Test-Time Sampling for Vision Language Action Models
di: Jang, Suhyeok, et al.
Pubblicazione: (2025)

Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts
di: Lee, Jihoon, et al.
Pubblicazione: (2025)

Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
di: Lee, Jeongjae, et al.
Pubblicazione: (2026)

Disentangling Hyperedges through the Lens of Category Theory
di: Lee, Yoonho, et al.
Pubblicazione: (2025)

Test-time Alignment of Diffusion Models without Reward Over-optimization
di: Kim, Sunwoo, et al.
Pubblicazione: (2025)

Unknown Domain Inconsistency Minimization for Domain Generalization
di: Shin, Seungjae, et al.
Pubblicazione: (2024)

Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
di: Park, Dongmin, et al.
Pubblicazione: (2024)

CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
di: Chung, Hyungjin, et al.
Pubblicazione: (2024)

Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
di: Shin, Seunghyeok, et al.
Pubblicazione: (2026)

Temporal Alignment Guidance: On-Manifold Sampling in Diffusion Models
di: Park, Youngrok, et al.
Pubblicazione: (2025)

Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
di: Kim, Minsang, et al.
Pubblicazione: (2026)

Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
di: Ahn, Donghoon, et al.
Pubblicazione: (2024)

R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
di: Song, Sanghyeob, et al.
Pubblicazione: (2026)

LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
di: Kim, Jeongchan, et al.
Pubblicazione: (2026)

DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
di: Kim, Jeongsol, et al.
Pubblicazione: (2024)

Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
di: Lee, Jaewoo, et al.
Pubblicazione: (2026)

Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
di: Um, Soobin, et al.
Pubblicazione: (2025)

Don't Play Favorites: Minority Guidance for Diffusion Models
di: Um, Soobin, et al.
Pubblicazione: (2023)

SEAL: Scaling to Emphasize Attention for Long-Context Retrieval
di: Lee, Changhun, et al.
Pubblicazione: (2025)

Temporal Dynamic Embedding for Irregularly Sampled Time Series
di: Kim, Mincheol, et al.
Pubblicazione: (2025)

Geometry-Aware Attention Guidance for Diffusion Models via Modern Hopfield Dynamics
di: Kim, Kwanyoung
Pubblicazione: (2026)