Our goal is for RSI data to become the primary dataset for pre-training and post-training the next generation of models. That only works if the data beats what labs use today on every axis that matters. Here is where it does, and the evidence for each claim.
What you getthat web data cannot give you.
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 corporaSee it in: Undergraduate STEM · Graduate MathExpert-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+ problemsSee it in: Graduate Math · Long-Document ReasoningScale 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 targetSee it in: Undergraduate STEM · Multimodal MathVerified, 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-validatedSee it in: Graduate Math · Undergraduate STEMBuilt 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 → scaleSee it in: Agent Trajectories · Long-Document ReasoningTraining-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 JSONLSee it in: Multimodal Math · Agent TrajectoriesDemand for data is outrunningthe supply of human text.
By 2024, foundation models had been trained on roughly 18 trillion tokens, close to the high-quality text openly available on the web. Web data grows by about 10% a year; training-data demand roughly doubles.
Crawling harder will not close that gap, and neither will more annotators. It closes when today's best models, working from a large base of human knowledge, produce the data for the next generation. That is recursive self-improvement applied to data.
The open pre-training corpora labs rely on today are mostly web crawls: unclear rights, uneven quality, low difficulty. That data trained the last generation of models. We are building what trains the next, starting with reasoning data already in production and a pre-training corpus in development.
Agents compose. Models verify.Humans set the bar.
Agent workflows run almost every internal step. We combine leading frontier models with our own compact, specialised models, and bring in specialists only for the hardest cases.
Human-authored library
200M+ documents (books, papers, patents, standards, exam banks) parsed by purpose-built models into Markdown and LaTeX, including formulas, tables and chemical structures.
Your target, as a spec
The capability you train for becomes subjects, levels, difficulty distribution, question types and schema.
Agents build new items
Agents work over the library to produce new items with answers and full step-by-step solutions.
Independent checks
SOTA-model verification, difficulty filtering, similarity deduplication, syntax and schema checks, random-sample audits.
“Isn’t training on model-generated dataa dead end?”
It is the question we hear most often. The research gives a specific answer: training on model output degrades models for three identifiable reasons, and improves them when those three are removed. Our engine is built to remove all three.
The tails disappear
A model samples its most likely outputs. Rare, hard and unusual cases get under-represented, and when the next model trains only on that output, the distribution narrows again. After a few generations the tails are gone.1
Keep the distribution anchored to real data. When synthetic data accumulates alongside the original human data instead of replacing it, error stays bounded no matter how many generations run.2
Every item starts from a specific human-authored source in a 200M-document library. Generation follows an explicit spec of subjects, levels and difficulty mix, not the model's favourite outputs. Difficulty filtering keeps the hard tail; deduplication removes templated near-copies. Our data is added to your human data, never a replacement for it.
Errors compound
Nothing checks the generated text. Mistakes become training targets, the next model learns them as truth, and produces more of them.1
Verify before training. Judging an answer is easier than producing one, so selection works even with imperfect verifiers. Model-generated solutions filtered for correctness outperform fine-tuning on human-written data, and AlphaGeometry reached olympiad level trained on 100M machine-generated, machine-verified examples.345
Independent state-of-the-art models, different from the one that composed the item, check every candidate. Items with verifiable answers are prioritised. Random-sample audits measure 98%+ accuracy, and you verify a pilot yourself before anything scales.
The loop is closed
In the studies where collapse occurs, each model learns only from its predecessor's output. No new information enters the loop, so it can only lose information.12
Bring information in from outside the loop: real data, stronger models, external checks. Every self-improvement result that works has at least one of these.234
This is not a model learning from itself. Today's strongest models, a human-authored library and independent verifiers together produce data that trains a different, next-generation model. New information enters at every step.
The limits, stated plainly. Verification is strong, not perfect. That is why we measure accuracy by audit rather than assume it, and why every engagement starts with a pilot that you evaluate with your own methods.
- Shumailov et al., “AI models collapse when trained on recursively generated data”, Nature 631, 2024
- Gerstgrasser et al., “Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data”, 2024
- Feng et al., “Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification”, ICLR 2025
- Singh et al. (Google DeepMind), “Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models”, 2023
- Trinh et al., “Solving olympiad geometry without human demonstrations” (AlphaGeometry), Nature 625, 2024
Claims you can check yourself.
- 98%+ sampled accuracy · 97%+ customer-validatedAccuracy is measured by random sampling and verification with state-of-the-art models. One custom question-bank delivery was validated by a major technology customer's own team at 97%+.
- Verify it your wayEvery engagement starts with a pilot sample you evaluate with your own methods before scaling. Public samples are on every data page.
- New items, clear ownershipData is composed fresh, deduplicated against sources and public corpora, and delivered under a commercial license with clear title.
- Training-readyMarkdown and LaTeX text with automated syntax checks, delivered as structured JSONL.
Where these advantagesshow up.
Graduate & Competition Mathematics
1M+problems→Focus line→ 100MUndergraduate STEM Reasoning
40M+problems→Focus line→ 10MMultimodal Mathematics
1M+image-text problems→AvailableCoding & Office Agent Trajectories
100K+trajectories per track→AvailableExpert Long-Document Reasoning
10K+RL tasks→AvailableMultilingual Speech for ASR & TTS
10M+hours→In developmentSynthetic Pre-training Corpus
In development→Frequently asked questions.
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.
See the engine's output.
Every data line has an inspectable public sample. For a pilot slice cut to your specification, send us a request.