Diversity-Aware Sampling

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20 papers6 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
Oct 15, 2026Kwanyoung Lee, SeungJu Cha, Yebin Ahn +2Diversity-Aware Sampling Diffusion Sampling
Hanyang University, South Korea
Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models
Oct 15, 2026Kostas Triaridis, Morris E. Sharp, Nebojsa JojicGenerative Modeling Diversity-Aware Sampling
Stony Brook University · Microsoft
JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling
Sep 27, 2026Guangxun Zhang, Brian Cai, Boxuan Zhang +2Generative Modeling Diversity-Aware Sampling
New York University · Stony Brook University · Rutgers University
Selecting Diverse SFT Traces Improves Post-RL Generalization
Sep 27, 2026Dylan Zhang, Mingyuan Wu, Jinning LiDiversity-Aware Sampling Reasoning Diversity
University of Illinois Urbana-Champaign, work done at Google · Google
Improving the Diversity of LLM Outputs without a Trade-off
Sep 27, 2026Ryoma SatoOutput Diversity in Language Models Language Model Decoding
National Institute of Informatics
Strategically Diverse Sampling for Self-Training
Sep 25, 2026Alexander Gurung, Esmeralda S. Whitammer, Mirella LapataSelf-Training for Language Models Diversity-Aware Sampling
School of Informatics, University of Edinburgh
XTC: Head-Aware Sampling by Excluding Top Choices
Aug 24, 2026Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2Language Model Decoding Diversity-Aware Sampling
Independent Researcher · Thoughtworks · New York University
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
Jul 2, 2026Disheng Liu, Tuo Liang, Chaoda Song +1Synthetic Data Augmentation Diversity-Aware Sampling
Department of Computer and Data Sciences Case Western Reserve University Cleveland, OH, USA
Variance-Tilted Diffusion Models for Diverse Sampling
Jun 20, 2026Iskander Azangulov, Leo Zhang, Kianoosh AshouritaklimiDoob H-Transform Diffusion Model Sampling
Department of Statistics, University of Oxford, Oxford, UK.
Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search
Jun 15, 2026Sidhaarth Murali, João Coelho, Jingjie Ning +3Agentic Search Test-Time Scaling
Carnegie Mellon University, United States · Instituto Superior Técnico and INESC-ID, University of Lisbon, Portugal · NOVA LINCS, NOVA School of Science and Technology, Portugal
Spokes: Optimizing for Diverse Pretraining Data Selection
Jun 13, 2026Clarence Lee, Yejin Choi, Luke Zettlemoyer +2Language Model Pretraining Diversity-Aware Sampling
DSO National Laboratories · Stanford University · University of Washington
Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection
May 19, 2026Fatemeh Pesaran Zadeh, Seyeon Choi, Xing Han Lù +2Diversity-Aware Sampling OOD Generalization
Seoul National University · McGill University · Mila – Quebec AI Institute +1
SAS: Semantic-aware Sampling for Generative Dataset Distillation
May 18, 2026Mingzhuo Li, Guang Li, Linfeng Ye +4Diversity-Aware Sampling Training Data Selection
Hokkaido University, N-14, W-9, Kita-Ku, Sapporo, 060-0814, Japan · University of Toronto, 27 King’s College Circle, Toronto, Ontario M5S 1A1, Canada · The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
Diverse Sampling in Diffusion Models with Divergence-Free Particle Guidance
May 7, 2026Gal Vinograd, Idan Achituve, Ethan FetayaDiffusion Model Guidance Diffusion Sampling
Faculty of Engineering Bar-Ilan University Ramat Gan, Israel · Independent Researcher
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
May 6, 2026Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6Diversity-Aware Sampling Data Selection
Department of Materials Science and Engineering, University of Toronto, Canada. · Vector Institute for Artificial Intelligence, Toronto, Canada · Department of Computer Science, Princeton University +2
A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
Apr 10, 2026Theo X. Olausson, Metod Jazbec, Xi Wang +4Diffusion Language Models Diversity-Aware Sampling
Massachusetts Institute of Technology · UvA-Bosch Delta Lab, University of Amsterdam · Johns Hopkins University +1
Rethinking Representativeness and Diversity in Dynamic Data Selection
Mar 5, 2026Yuzhe Zhou, Zhenglin Hua, Haiyun Guo +1Diversity-Aware Sampling Data Selection
Southeast University, Nanjing, China · Chinese Academy of Sciences, Beijing, China
Free Lunch for Pass@ $k$ ? Low Cost Diverse Sampling for Diffusion Language Models
Mar 5, 2026Sean Lamont, Christian Walder, Paul Montague +2Diversity-Aware Sampling Output Diversity in Language Models
Australian National University · Defence Science and Technology Group · Google DeepMind +1
A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT
Feb 25, 2026Louis Estève, Christophe Servan, Thomas Lavergne +1Language Model Pretraining Diversity-Aware Sampling
Université Paris-Saclay, CNRS, LISN
Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
Feb 17, 2026Salim Hafid, Manon Berriche, Jean-Philippe CointetDiversity-Aware Sampling Data Selection
m´edialab, Sciences Po Paris, France.

Oct 15, 2026J/K move · Enter open · S save

Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling

Kwanyoung Lee, SeungJu Cha, Yebin Ahn, Sungho Koh+1

Hanyang University, South Korea

Diversity-Aware Sampling Diffusion Sampling T2I Diffusion Models

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Strong guidance in text-to-image (T2I) diffusion models concentrates sampling toward high-density modes of the conditional distribution, collapsing independently sampled noise vectors onto nearly identical visual prototypes. This limits utility for tasks that demand distributional breadth, most critically synthetic data augmentation. Diversity, however, is a collective property of the whole batch: a set can preserve every pairwise distance yet still collapse onto a low-dimensional subspace. Existing sum-of-pairwise noise-optimization and repulsion objectives respond only weakly to this higher-order failure. We propose DetNO (Determinant Noise Optimization), which formulates diverse generation as RKHS volume maximization on the initial noise, maximizing the log-determinant of an RBF Gram matrix over one-step Tweedie predictions. This set-level functional equals the squared batch volume; its repulsion is strongest when the batch is most collapsed and self-attenuates as diversity is achieved. An anchor-based fidelity term keeps the optimized noises near plausible regions, and Lagrangian relaxation gives a two-term objective with a single hyperparameter

λ

governing the diversity-quality trade-off, without training or sampler modification. Across three backbones (SD1.5, SDXL, SD3), DetNO achieves leading intra-batch diversity in VendiScore and Mean Self-Similarity, and a downstream few-shot classification experiment confirms that broader mode coverage brings statistically significant accuracy gains for synthetic data augmentation in the low-shot regime. Fixing the pipeline and varying only the batch objective, both RBF-based aggregators outperform InfoNCE, and the log-determinant attains the best downstream accuracy on both benchmarks. Its diversity advantage over InfoNCE widens as the batch grows. Code is available at https://github.com/mobled37/DetNO.