Most AI labs ship models. DeepData ships data. We train models and build agents too, but what we deliver is training data: expert-level, copyright-clear and verified, produced at compute scale through recursive self-improvement.
We want RSI data to become the primary dataset for both pre-training and post-training. Today's open pre-training corpora are mostly crawled from the web. Their rights are unclear, their quality is uneven, and most of it is far too easy. That data trained the last generation of models. We use today's SOTA models and existing human data to build the complete dataset the next generation needs.
In 2022, training data meant crawling the web, licensing books and paying people to write answers. Today's models already reason above graduate level. They need data harder than anything they have seen, and far more of it.
Most datasets offer one of the three. Training the next generation of models requires all of them together. That is what RSI data delivers.
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 corporaSee it in: Undergraduate STEM · Graduate MathFrontier 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+ problemsSee it in: Graduate Math · Long-Document ReasoningProduction 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 targetSee it in: Undergraduate STEM · Multimodal MathThis problem comes from our graduate & competition mathematics line: a newly composed problem with a full, verified solution, in training-ready LaTeX.
Determine all solutions to the triple exponential congruence .
Since the multiplicative order of modulo is , the congruence holds if and only if the exponents are congruent modulo , i.e., . Working modulo , we have , so . Additionally, , thus . This evaluates to if and only if is even. Since is odd, is even exactly when is even. Therefore, the congruence holds precisely when is an even integer.
Internal agents work over our proprietary database, compose data to each customer's requirements, and verify every item before it ships.
A digital library of 200M+ human-authored documents, 2 PB+, much of it not publicly available.
Grounded in the library, never copied from it, to your subject, level and difficulty targets.
SOTA-model verification, similarity deduplication, LaTeX and schema checks.
Structured JSONL, sliced for SFT, RL or evaluation. Pilot first, then scale.
Three focus lines in reasoning data, plus agent, long-document and speech data, and a pre-training corpus in development. Every line has a page with inspectable samples.
Research-grade and elite-competition problems with rigorous, verifiable solutions.
1M+problems→Focus line→ 100MMathematics, physics, chemistry, biology and computer science at university level and above.
40M+problems→Focus line→ 10MUniversity-level problems where the figure is part of the problem: graphs, diagrams, geometry.
1M+image-text problems→AvailableComplete multi-turn agent runs: software-engineering tasks and long-horizon office work.
100K+trajectories per track→AvailableFinance, legal and healthcare tasks on real long documents, targeting known model failures.
10K+RL tasks→AvailablePrecisely aligned speech and text across major and low-resource languages and dialects.
10M+hours→In developmentTextbook-style, long-form reasoning text produced by the RSI engine, for next-generation pre-training.
In development→Structured, step-by-step solutions to learn from. → Undergraduate STEM
Hard problems with checkable answers. → Graduate Math
Tasks that probe the limits of current models. → Long-Document Reasoning
Problems where the figure carries the information. → Multimodal Math
End-to-end coding and office trajectories. → Agent Trajectories
A copyright-clear synthetic corpus, in development. → Pre-training
We work B2B with model developers, research laboratories and universities. Each engagement is cut to fit, and you decide when the data is good enough.
Define the capability, subjects, difficulty mix, levels and format.
Receive a sample slice to evaluate with your own methods.
Your team validates against agreed accuracy thresholds.
Production delivery, from thousands to tens of millions of items.
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).
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.
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%.
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.
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.
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.
Tell us the capability you are training for. We will scope a slice by subject, difficulty and format, and send a pilot sample for your own evaluation.