Latest Synthetic Data Research Papers

The newest Synthetic Data papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Synthetic Data so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
  Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong Liu · arXiv · Sep 8, 2026

  False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combining a 1D SE-ResNet with ICU-realistic data augmentations and a physics-informed auxilia…

• LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
  Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026

  Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…

• Learning a Size-Weight Frontier for Synthetic-Augmented Inference
  Chengpiao Huang, Kaizheng Wang · arXiv · Aug 28, 2026

  Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference. We develop a general framework for synthetic-augmented infe…

• CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
  Sil Hamilton, Albert Yu Sun, Oscar J. Romero, Carl-Leander Henneking et al. · arXiv · Aug 27, 2026

  LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to share internal communications, and synthetic datasets have been overly simple. We present Co…

• Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories
  Pedro Cadahia Delgado · arXiv · Aug 21, 2026

  Short observational pricing panels can contain many observations while offering only a small number of distinct price movements. This paper studies the inferential consequences of that distinction in a synthetic data-generating process cali…

• On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
  Qinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang et al. · arXiv · Aug 18, 2026

  Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods…

• Boosting Data Augmentation with Stochastic Weight Averaging
  Longde Huang, Axel Flinth, Jan E. Gerken · arXiv · Aug 14, 2026

  The symmetries of a learning task have become an important factor in designing modern deep learning solutions. Data augmentation is a straightforward and effective way of incorporating symmetries into a generic neural network. Recent result…

• OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
  Robin Trombetta, Carole Lartizien · arXiv · Aug 6, 2026

  The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors from large volumes to characterize pathologies. Data augmentation is a technique widely re…

• Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation
  Seyed Kahaki, Shijie Li, Weijie Chen, Nicholas Petrick · arXiv · Aug 4, 2026

  Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications. This …

• Synthetic data generation framework for quality control automation in gravure printing
  Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali, Andrea Trombin · arXiv · Jul 23, 2026

  Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printin…

• ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
  Krishna Teja Chitty-Venkata, Murali Emani · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  We develop ImageNet-Think-250K, a multimodal reasoning dataset designed to aid the development of Vision Language Models (VLMs) with explicit reasoning capabilities. Our dataset is built on 250,000 images from ImageNet-21k dataset, providin…

• ESDAE: Evaluating Synthetic Data for Agent Evaluation
  Shuaiqi Wang, Aadyaa Maddi, Zinan Lin, Giulia Fanti · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Agent evaluation is often performed on static datasets of execution trajectories, but real traces may be sensitive, proprietary, or too small to support comprehensive testing. Practitioners may therefore replace or augment real datasets wit…

• SynQuE: Estimating Synthetic Dataset Quality Without Annotations
  Arthur Chen, Victor Zhong · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  We introduce and formalize the Synthetic Dataset Quality Estimation (SYNQUE) problem: ranking synthetic datasets by their expected real-world task performance using only limited unannotated real data. This addresses a critical and open chal…

• Less is More: Adaptive Coverage Sampling for Synthetic Training Data
  Sasan Tavakkol, Max Springer, Mohammadhossein Bateni, Vincent Cohen-Addad et al. · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Large Language Models (LLMs) enable rapid generation of synthetic training data for downstream classifiers, offering a solution when human-labeled data is costly, scarce, or time-sensitive. However, synthetic datasets suffer from systematic…

• EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
  Amin Banayeeanzade, Qingchuan Yang, Deqing Fu, Spencer Hong et al. · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have …

• Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
  Bingji Yi, Qiyuan Liu, Yuwei Cheng, Haifeng Xu · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Synthetic data has been increasingly used to train frontier generative models. However, recent study raises key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model perfor…

• Learning from Synthetic Data Improves Multi-hop Reasoning
  Anmol Kabra, Yilun Yin, Albert Gong, Kamilė Stankevičiūtė et al. · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Reinforcement Learning (RL) has been shown to significantly boost reasoning capabilities of large language models (LLMs) in math, coding, and multi-hop reasoning tasks. However, RL fine-tuning requires abundant high-quality verifiable data,…

• PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
  Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi et al. · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. W…

• Motion Capture is Not the Target Domain: Scaling Synthetic Data for Learning Motion Representations
  Firas Darwish, George Nicholson, Aiden Doherty, Hang Yuan · ICLR 2026 Workshop DATA-FM · Mar 2, 2026

  Synthetic data offers a compelling path to scalable pretraining when real-world data is scarce, but models pretrained on synthetic data often fail to transfer reliably to deployment settings. We study this problem in full-body human motion,…

• WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
  Kuan Li, Zhongwang Zhang, Huifeng Yin, Rui Ye et al. · arXiv.org · Sep 16, 2025

  Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on extremely complex information-seeking benchmarks such as Bro…

• BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining
  Pratyush Maini, Vineeth Dorna, Parth Doshi, Aldo Carranza et al. · arXiv.org · Aug 14, 2025

  Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, the use of synthetic data for pretraining has emerged as a pro…

• A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
  Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu, Asma Ben Abacha et al. · Annual Meeting of the Association for Computational Linguistics · May 15, 2025

  High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical do…

• Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use
  Anna Goldie, Azalia Mirhoseini, Hao Zhou, Irene Cai et al. · arXiv.org · Apr 7, 2025

  Reinforcement learning has been shown to improve the performance of large language models. However, traditional approaches like RLHF or RLAIF treat the problem as single-step. As focus shifts toward more complex reasoning and agentic tasks,…

• Scaling Laws of Synthetic Data for Language Models
  Zeyu Qin, Qingxiu Dong, Xingxing Zhang, Li Dong et al. · arXiv.org · Mar 25, 2025

  Large language models (LLMs) achieve strong performance across diverse tasks, largely driven by high-quality web data used in pre-training. However, recent studies indicate this data source is rapidly depleting. Synthetic data emerges as a …

• Synthetic Data Generation Using Large Language Models: Advances in Text and Code
  Mihai Nadǎş, Laura Dioşan, Andreea Tomescu · IEEE Access · Mar 18, 2025

  This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment o…

• Synthetic data generation: a privacy-preserving approach to accelerate rare disease research
  Jorge M. Mendes, Aziz Barbar, Marwa Refaie · Frontiers Digit. Health · Mar 18, 2025

  Rare disease research faces significant challenges due to limited patient data, strict privacy regulations, and the need for diverse datasets to develop accurate AI-driven diagnostics and treatments. Synthetic data—artificially generated da…

• Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
  Xu Liu, Taha İbrahim Aksu, Juncheng Liu, Qingsong Wen et al. · arXiv.org · Mar 14, 2025

  Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), …

• Unlocking Post-hoc Dataset Inference with Synthetic Data
  Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic · ICLR 2025 Workshop Data Problems Poster · Mar 6, 2025

  The remarkable capabilities of large language models stem from massive internet-scraped training datasets, often obtained without respecting data owners' intellectual property rights. Dataset Inference (DI) enables data owners to verify una…

• Synthetic Data is an Elegant GIFT for Continual Vision-Language Models
  Bin Wu, Wuxuan Shi, Jinqiao Wang, Mang Ye · Computer Vision and Pattern Recognition · Mar 6, 2025

  Pre-trained Vision-Language Models (VLMs) require Continual Learning (CL) to efficiently update their knowledge and adapt to various downstream tasks without retraining from scratch. However, for VLMs, in addition to the loss of knowledge p…

• Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10
  Ranjan Sapkota, Manoj Karkee · IFAC-PapersOnLine · Feb 26, 2025

  This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards based on the model training completed entirely on synthetic ima…