Adaptive Sampling

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Adaptive sampling studies how to choose which examples, measurements, or computational steps to spend a limited budget on. Common approaches use uncertainty, prediction error, information gain, or difficulty to guide active learning, Bayesian experimental design, bandit allocation, and adaptive numerical solvers; learned policies, including reinforcement learning, are also used. Applications range from scientific experiments and sensor acquisition to diffusion-model sampling and test-time reasoning with language models. A central challenge is ensuring that the signals used to prioritize samples reliably improve the final task without sacrificing coverage, introducing bias, or costing more to compute than they save.

Latest papers

202 papers37 papers in the last four weeks, up 106% on the four weeks before. 0.2% of all new papers.

Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Mar 8, 2026Suorong Yang, Fangjian Su, Hai Gan +5Data Selection Training Data Selection
National University of Singapore · Nanjing University · Nanyang Technological University
Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
Mar 6, 2026Akshay Govind Srinivasan, Balaji SrinivasanNeural PDE Solvers PDE Solving
Indian Institute of Technology, Madras Chennai, TN 600107, India
Observationally Informed Adaptive Causal Experimental Design
Mar 4, 2026Erdun Gao, Liang Zhang, Jake Fawkes +5Active Experimental Design Causal Inference
Australian Institute for Machine Learning, Adelaide University · Hong Kong University of Science and Technology (Guangzhou) · University College London +3
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Mar 4, 2026Pengfei Tong, Siyuan Chen, Chenwei Zhang +4Recommender Systems Learning to Rank
ByteDance
DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
Mar 1, 2026Haowen Gao, Zhenyu Zhang, Liang Pang +7Group Relative Policy Optimization RL for VLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Kuaishou Technology, Beijing, China
Learning Positive-Incentive Point Sampling in Neural Implicit Fields for Object Pose Estimation
Feb 23, 2026Yifei Shi, Boyan Wan, Xin Xu +1Object Pose Estimation Implicit Neural Representations
College of Intelligence Science and Technology, National University of Defense Technology, China · College of Computer Science, National University of Defense Technology, China
Learning Compact Video Representations for Efficient Long-form Video Understanding in Large Multimodal Models
Feb 19, 2026Yuxiao Chen, Jue Wang, Zhikang Zhang +8Adaptive Video Frame Sampling Long-Video Understanding
Amazon AGI
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
Feb 19, 2026Pedro Dall'Antonia, Tiago da Silva, Daniel Csillag +2Generative Flow Networks Adaptive Sampling
School of Applied Mathmatics, Getulio Vargas Foundation · MBZUAI
Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Feb 19, 2026Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik +1Adaptive Sampling Active Learning
University of Michigan, Ann Arbor, MI, USA · Washington University in St. Louis, St. Louis, MO, USA
Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization
Feb 19, 2026Sumedh RasalEfficient Language Model Training Adaptive Sampling
Georgia Institute of Technology
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
Feb 18, 2026Zhangyi Liu, Huaizhi Qu, Xiaowei Yin +4Inference-Time Compute Allocation Self-Consistency in Language Models
Stanford University · UNC at Chapel Hill · Yale University +1
Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification
Feb 17, 2026Eider Garate-Perez, Kerman López de Calle-Etxabe, Oihana Garcia +3Surrogate Modeling Computational Materials Science
Intelligent Information Systems, Tekniker, Iñaki Goenaga 5, Eibar (20600) Spain · Faculty of Informatics UPV-EHU, Paseo Manuel de Lardizabal 1, Donostia (20700) Spain · Department of Computer Sciences and Artificial Intelligence, UPV-EHU, Donostia (20700) Spain +1
LLM-as-Judge on a Budget
Feb 17, 2026Aadirupa Saha, Aniket Wagde, Branislav KvetonLLM Evaluation LLM-as-a-Judge
UIC · Adobe
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
Feb 13, 2026Sangwoo Jo, Sungjoon ChoiDiffusion Model Sampling Diffusion Sampling
Department of Artificial Intelligence, Korea University, Seoul, South Korea.
Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
Feb 2, 2026Sanna Jarl, Jens Sjölund, Jonathan J. S. Scragg +1Active Learning Adaptive Sampling
Department of Material Science & Engineering Uppsala University RISE · Department of Information Technology Uppsala University · RISE
HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning
Jan 30, 2026Weiqi Wang, Xin Liu, Binxuan Huang +13RL for Language Models RL for Language Model Reasoning
Stores Foundational AI, Amazon Inc., Palo Alto, CA, USA · Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China
ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
Jan 26, 2026Yilie Huang, Wenpin Tang, Xunyu ZhouDiffusion Sampling Adaptive Sampling
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. · Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University, New York, NY 10027, USA.
Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
Jan 6, 2026David Hartmann, Lena Pohlmann, Lelia Hanslik +3LLM Auditing Algorithmic Fairness
Weizenbaum Institut Berlin · Technische Universität Berlin · FIZ Karlsruhe +2
Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
Dec 22, 2025Xiaolong Wu, Qifeng LiaoNeural PDE Solvers PDE Solving
School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
Aug 24, 2025Yinsong Wang, Quan Zeng, Xiao Liu +1Adaptive Sampling Adaptive Learning
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta & 30332, USA.
Model Predictive Task Sampling for Efficient and Robust Adaptation
Jan 19, 2025Qi Wang, Zehao Xiao, Yixiu Mao +4Adaptive Sampling Distribution Shift Robustness
Department of Automation, Tsinghua University · Informatics Institute, University of Amsterdam
Grammar-Aligned Decoding
May 31, 2024Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick +2Constrained Generative Modeling Constrained Decoding
University of Wisconsin-Madison · University of California San Diego

Mar 8, 2026J/K move · Enter open · S save

Data Agent: Learning to Select Data via End-to-End Dynamic Optimization

Suorong Yang, Fangjian Su, Hai Gan, Ziqi Ye+4

National University of Singapore · Nanjing University · Nanyang Technological University

Data Selection Training Data Selection Data-Efficient Learning Adaptive Sampling

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Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted metrics or static/snapshot-based criteria to estimate sample importance, limiting scalability across learning paradigms and making it difficult to capture the evolving utility of data throughout training. To address this challenge, we propose Data Agent, an end-to-end dynamic data selection framework that formulates data selection as a training-aware sequential decision-making problem. The agent learns a sample-wise selection policy that co-evolves with model optimization, guided by a composite reward that integrates loss-based difficulty and confidence-based uncertainty signals. The reward signals capture complementary objectives of optimization impact and information gain, together with a tuning-free adaptive weighting mechanism that balances these signals over training. Extensive experiments across a wide range of datasets and architectures demonstrate that Data Agent consistently accelerates training while preserving or improving performance, e.g., reducing costs by over 50% on ImageNet-1k and MMLU with lossless performance. Moreover, its dataset-agnostic formulation and modular reward make it plug-and-play across tasks and scenarios, e.g., robustness to noisy datasets, highlighting its potential in real-world scenarios. Code is available at https://github.com/Jackbrocp/Data-Agent.