# DeepData > DeepData is a data lab building RSI-synthesized training data for next-generation pre-training and post-training: copyright-clear, verified, expert-level, and scaled by compute. Models went deep. Now data must go deeper. DeepData is a data lab: we train models and build agents, but what we deliver is training data for AI labs. Our goal is to make RSI (recursive self-improvement) data the primary dataset for pre-training and post-training the next generation of models. Agents compose new, expert-level items from a 200M-document human-authored library; independent state-of-the-art models verify every item. Contact: contact@deepdatalab.ai. ## Data lines - [Graduate & Competition Mathematics](https://deepdatalab.ai/data/advanced-math/): 1M+ problems. Research-grade and elite-competition problems with rigorous, verifiable solutions. - [Undergraduate STEM Reasoning](https://deepdatalab.ai/data/stem/): 40M+ problems, target 100M. Mathematics, physics, chemistry, biology and computer science at university level and above. - [Multimodal Mathematics](https://deepdatalab.ai/data/multimodal-math/): 1M+ image-text problems, target 10M. University-level problems where the figure is part of the problem: graphs, diagrams, geometry. - [Coding & Office Agent Trajectories](https://deepdatalab.ai/data/agent-trajectories/): 100K+ trajectories per track. Complete multi-turn agent runs: software-engineering tasks and long-horizon office work. - [Expert Long-Document Reasoning](https://deepdatalab.ai/data/long-document/): 10K+ RL tasks. Finance, legal and healthcare tasks on real long documents, targeting known model failures. - [Multilingual Speech for ASR & TTS](https://deepdatalab.ai/data/speech/): 10M+ hours. Precisely aligned speech and text across major and low-resource languages and dialects. - [Synthetic Pre-training Corpus](https://deepdatalab.ai/data/pretraining/): In development. Textbook-style, long-form reasoning text produced by the RSI engine, for next-generation pre-training. ## Why RSI data - Copyright-clear by construction: Every item is newly composed by our agents and deduplicated against its sources and public corpora. You license data with clear title, not scraped text with open questions. (Similarity-deduplicated against sources and public corpora) - Expert-level difficulty: Frontier models already handle common knowledge. We filter for problems that remain hard for them, and prioritise items current models get wrong but whose answers can be verified. (Graduate & competition math: 1M+ problems) - Scale that tracks compute: Production is driven by agents and compute, not annotator headcount. That is how a line grows from thousands of items to tens of millions, and keeps growing. (Undergraduate STEM: 40M+ today, 100M target) - Verified, not just generated: Independent state-of-the-art models verify every candidate; random-sample audits measure accuracy above 98% on reasoning data. A customer's own team validated one delivery at 97%+. (98%+ sampled accuracy · 97%+ customer-validated) - Built to your specification: There is no fixed SKU. Each engagement starts from the capability you are training for, and we produce the slice that serves it. (Scope → pilot → acceptance → scale) - Training-ready on arrival: Records arrive with answers and full step-by-step solutions, in clean Markdown and LaTeX with automated syntax and schema checks. They drop straight into SFT, RL and evaluation pipelines. (100% Markdown/LaTeX · schema-checked JSONL) ## Pages - [Vision](https://deepdatalab.ai/): why next-generation models need RSI data. - [Approach](https://deepdatalab.ai/approach/): advantages, evidence, how the engine works, robustness against model collapse. - [Data catalog](https://deepdatalab.ai/data/): all data lines with public samples. - [Request samples](https://deepdatalab.ai/contact/): request form for samples, pilot slices and custom datasets (or email contact@deepdatalab.ai). ## FAQ ### What is DeepData? DeepData is a data lab. We train models and build agents, but what we deliver is training data for AI labs: expert-level, copyright-clear, verified data produced at compute scale through recursive self-improvement (RSI). ### What is RSI data? RSI (recursive self-improvement) data is produced by using today's state-of-the-art models, together with a large base of human-authored material, to build the training data for the next generation of models. At DeepData, agents compose new items from a 200M-document human library, and independent frontier models verify every item. ### Who provides graduate-level and competition math training data? DeepData provides 1M+ graduate and competition-level mathematics problems with rigorous step-by-step solutions, plus 40M+ undergraduate STEM problems and 1M+ multimodal math problems, all with sampled accuracy above 98%. ### Is synthetic training data copyright-safe? DeepData items are newly composed rather than copied, and deduplicated against their sources and public corpora. Data is delivered under a commercial license with clear title. Customers' legal teams should review the license terms for their jurisdiction. ### Does training on model-generated data make models worse (model collapse)? Only under specific conditions. Collapse is observed when each model trains only on the unverified output of the previous one, with no real data left: rare cases disappear and errors compound (Shumailov et al., Nature 2024). It is avoided when synthetic data accumulates alongside real data instead of replacing it (Gerstgrasser et al., 2024), and when outputs are verified before training (Feng et al., ICLR 2025); verified model-generated solutions can even outperform human-written training data (Singh et al., 2023). DeepData anchors every item in human-authored sources, verifies it with models other than the one that composed it, and filters for difficulty and novelty. ### How accurate is the data? Reasoning data measures above 98% accuracy on random-sample audits verified by state-of-the-art models. One custom delivery was validated by the customer's own team at 97%+. Every engagement starts with a pilot that you evaluate yourself. ### What formats are delivered? Structured JSONL with Markdown and LaTeX for text; PNG figures for multimodal data; standard chat-message format for agent trajectories; audio with aligned transcripts for speech. ### How do I get a sample? Each data line has a public sample on its page. For a pilot slice cut to your specification, email contact@deepdatalab.ai.