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Synthetic Data
Artificially generated data created to augment training sets, protect privacy, or simulate rare scenarios.
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- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
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Training Data Augmentation Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization. Vision & Multimodal Generative Adversarial Network A framework where two networks (generator and discriminator) compete, with the generator learning to create realistic data. Shipping AI Differential Privacy A mathematical framework for quantifying and limiting privacy loss when releasing information about datasets. Vision & Multimodal Diffusion Model A generative model that learns to denoise data, achieving state-of-the-art image generation (Stable Diffusion, DALL-E 2). Agents & RL Domain Randomization Training with randomized simulation parameters to improve transfer to real-world environments.
In the research
All papers →5 papers that build on Synthetic Data .
Frontier · Nov 2025 · 197 citations SAM 3D: 3Dfy Anything in Images Breaking the 3D data bottleneck makes single-photo 3D practical for real images. 
Frontier · Aug 2025 · 167 citations R-Zero: Self-Evolving Reasoning LLM from Zero Data Self-generated training data is one route around the human-data bottleneck. 
Frontier · Sep 2025 · 74 citations WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents Hard synthetic data, not model size, turns out to be the lever for web agents. 
Frontier · Sep 2025 · 66 citations WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents A clear pattern for keeping long research agents from drowning in their own context. 
Frontier · Sep 2025 · 60 citations WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning A detailed open recipe for the hardest information-seeking tasks.