Verifiable training data
for the next generation of LLMs
Verified Data for Frontier Models.
TokenDaddy was founded by members of AI training labs and data experts from major internet companies to meet the data demands of tomorrow's LLM training. We deliver pre-training corpora, RL environments and post-training data — and we measure delivery by real gains in model capability.
Pre-training
Clean, compliant, well-balanced corpora at scale — the foundation of a model's knowledge and language.
RL Environments
Runnable sandboxes, task suites and automated graders that let models improve on real tasks.
Post-training Data
Expert demonstrations, preference comparisons, rubrics and verifiable hard problems that raise the ceiling on reasoning and alignment.
Training research and data engineering,
one team
TokenDaddy's core team comes from AI training labs and the data teams of China's leading internet companies. We know which data actually moves model capability — and we have the engineering and operations experience to deliver it at scale, to a consistent standard. We exist to meet the data demands of tomorrow's LLM training.
Training Lab Experience
Our core members have trained LLMs hands-on, so we define data from the model's point of view.
- Full-stack: pre-training, SFT, preference alignment and RL
- Know how data really affects loss, evals and capability
- Value data by training ablations, not label counts
- Speak the same technical language as your research team
Big Tech Data Expertise
Drawn from the data teams of leading internet companies, with deep experience in large-scale data production and governance.
- Large-scale data collection, cleaning and governance
- Annotation ops, expert network management and multi-tier QA
- Data security, privacy compliance and cross-border workflows
- Platform tooling for stable, reproducible delivery
Built for the Next Training Paradigm
LLM training is shifting from more data to better data and environments. That is what we are built for.
- Focused on RL environments, agent trajectories and verifiable rewards
- Tracking frontier training methods to build tomorrow's data formats early
- Research and production under one roof — shorter cycles from sample to delivery
- Native strength in Chinese-language and China-specific data
| Dimension | Traditional Labeling Vendors | TokenDaddy |
|---|---|---|
| Success metric | Label volume and spot-check accuracy | Measurable gains in target model capabilities |
| Team | Project managers + crowdsourced labelers | Training researchers + data engineers + domain experts |
| Quality proof | Spot-check reports | Agreement metrics + verifier results + decontamination reports + training ablations |
| Data types | Static labeled data | Corpora, RL environments, agent trajectories, verifiable hard problems |
| Collaboration | Executes against a ticket | Co-defines data specs and acceptance metrics with your research team |
Corpora at scale — traceable and well-balanced
From sourcing, licensing and cleaning to mixing and validation, we deliver data ready to drop into your training pipeline. Every package ships with a source manifest, chain of license and quality report.
Web Corpus Cleaning & Refinement
Turn raw crawl data into high-quality training text.
- Main-content extraction, language ID, format normalization
- Exact dedup + MinHash near-dedup (document / paragraph level)
- Quality-classifier scoring and tiered filtering
- Filtering of harmful content, ads and low-quality boilerplate
Licensed Content & Chain of Title
We work with publishers, journals, media outlets and professional bodies to source high-value text cleared for commercial use.
- Books, academic papers, news, industry reports
- License negotiation, contracts and usage-scope management
- Per-item provenance records that stand up to copyright review
PII & Sensitive Data Redaction
Remove or replace personally identifiable information before training to reduce compliance risk.
- Detection and replacement of names, phone numbers, ID numbers, addresses and emails
- Dedicated rules for Chinese data (national IDs, mobile numbers, license plates, etc.)
- Redaction spot checks and miss-rate reports
Multilingual & Low-Resource Corpora
Build capability beyond English, with a focus on low-resource languages.
- High-quality Chinese corpora (incl. Traditional Chinese and written dialects)
- Low-resource languages across Southeast Asia, Central Asia, the Middle East, Africa and more
- Parallel corpora and translation pairs
- Native-speaker quality checks
Code Corpora
Pre-training data for coding capability, balancing quality with license compliance.
- Open-source repo collection and license filtering
- Repo-level dedup; removal of auto-generated code and secrets
- Linked code–docs–issue data
- Compilability / executability filtering
Domain-Specific Corpora
Domain knowledge for industry models, with controlled sources and precise terminology.
- Finance, healthcare, law, industrial manufacturing, energy and more
- Structuring of regulations, standards, textbooks and technical manuals
- High-accuracy parsing of tables, formulas, PDFs and scans
Synthetic Data
Where real-world data falls short, fill the gap with model-generated, verified data.
- Textbook-style rewriting, Q&A and exercise conversion
- Long-context packing and retrieval task construction
- Diversity control and fact-checking to avoid model self-repetition
Image-Text & Document Data
Well-aligned image-text data for multimodal pre-training.
- Image-text pair collection and relevance filtering
- Interleaved image-text documents (web pages, textbooks, manuals)
- Structured annotation of OCR documents, charts and tables
Data Mixing & Decontamination
We don't just supply data — we help confirm it's worth training on.
- Mix recommendations by domain / language / quality tier
- n-gram decontamination against major benchmarks
- Ablation runs on small open-source models, comparing metrics with and without the data
Runnable, gradable, hack-resistant training environments
Reinforcement learning environments for agents and reasoning models: containerized sandboxes + tiered task suites + automated graders. Reproducible, parallelizable, and continuously expanded with new tasks.
Code Repository Environments
Bug-fix, feature and refactoring tasks built on real repositories.
- End-to-end issue → patch tasks with runnable tests
- Multi-language, multi-framework repos with containerized dependencies
- Dual grading: hidden tests + regression tests
Web Interaction Environments
Models complete multi-step web tasks such as searching, filling forms, placing orders and comparing prices.
- Resettable website replicas with controllable state
- E-commerce, travel, government services, enterprise back-office and more
- Dual grading on task completion and process compliance
Desktop & OS Environments
Desktop application tasks for computer use.
- Office apps, file management, system settings
- Windows / Linux VM snapshots and resets
- Dual observations: screenshots + accessibility tree
Tool & API Environments
Train models to select, chain and call tools correctly.
- Simulated backend services and MCP toolsets
- Multi-tool orchestration, error handling and retry scenarios
- Grading on argument correctness and call order
Business Workflow Environments
Real-world workflows turned into trainable environments for enterprise agents.
- CRM, ERP, support tickets, expense reimbursement, HR processes
- China-specific scenarios: WeCom, e-commerce back-office, tax invoices
- Multi-role workflows with permissions, approvals and exception branches
Math & Science Problem Environments (RLVR)
Hard problems with reference answers that can be graded programmatically.
- Competition-level math, physics, chemistry and algorithm problems
- Answer normalization and equivalence checks (numbers, expressions, sets)
- Difficulty tiers and pass-rate calibration
Reward Functions & Graders
Tasks without a reference answer still need a reliable reward signal.
- Programmatic grader development
- Combined rubric + LLM-judge grading, calibrated against human raters
- Reward model training data preparation
Reward Hacking Audits
Find the loopholes models exploit to collect reward without solving the task, and fix them before delivery.
- Adversarial testing of graders (test tampering, hard-coded answers, etc.)
- Sampled review of training trajectories
- Exploit fixes and regression verification
Environment Engineering & Operations
Environments stay stable after delivery and keep growing.
- Docker containerization with high-concurrency rollout support
- Task versioning and difficulty-curve tuning
- Custom eval benchmarks (strictly isolated from training tasks)
Expert-produced, expert-reviewed, verifiable data
Produced by an expert network of PhDs, engineers, physicians, lawyers and other practitioners, covering SFT, preference alignment, RL and safety evaluation.
SFT Demonstrations
High-quality instruction–response pairs that set the standard for great answers.
- Multi-turn dialogue, instruction following, complex format constraints
- Long-form writing, summarization, rewriting, translation
- Tone and format tailored to your style guide
Expert Reasoning Traces
Complete solutions and thought processes written by experts.
- Step-by-step derivations in math, science, coding and logic
- Including trial and error, self-checking and self-correction
- Step-level correctness labels (process rewards)
Preference Data (RLHF / DPO)
Compare and rank model responses to train preference and reward models.
- Pairwise comparison, multi-way ranking, Likert ratings
- Per-dimension scores for helpfulness, honesty, safety and more
- Rationales included for easy review and reuse
Rubric Writing & Scoring
Actionable rubrics for open-ended tasks — and scoring against them.
- Task-specific criteria, weights and deduction rules
- Expert peer review and gradability testing of rubrics
- Item-by-item scoring of model outputs against the rubric
Verifiable Hard Problems (RLVR)
Problems frontier models can't yet solve, with answers that can be verified programmatically.
- Original problems from graduate to competition level
- Reference answers or test cases included
- Adversarially filtered against frontier models to keep only the truly hard ones
Agent Trajectories
Complete records of experts completing tasks with real tools in real environments.
- Trajectories across coding, browser, desktop and business systems
- Observation, reasoning, action and outcome at every step
- Failure trajectories and recovery demonstrations
Domain Expert Data
Specialist data produced by licensed or senior practitioners.
- Medicine, law, finance, accounting, engineering, scientific research
- Expert Q&A, case analysis, professional document drafting
- Key facts cross-reviewed by two experts
Red Teaming & Safety Data
Systematically surface a model's safety gaps — and supply the data to fix them.
- Attack samples: jailbreaks, manipulation, bias, privacy leakage and more
- Demonstrations of the line between safe refusal and helpful response
- Compliance eval sets tailored to regional regulations
Chinese & Localization Data
Improve a model's grasp of Chinese context and local rules.
- Chinese writing styles, idioms and allusions, internet slang
- Knowledge of Chinese regulations, industry standards and administrative procedures
- Adaptation to the culture and language conventions of multiple regions
Human Evaluation & Custom Eval Sets
Quantify progress on target capabilities with expert evaluation.
- Blind comparisons across model versions
- Private eval sets, built with strict leakage controls
- Capability gap analysis and next-round data recommendations
Every batch ships with proof of quality
Quality is TokenDaddy's core advantage. Seven stages take every project from requirements to delivery, and each one leaves an auditable record.
Scoping
Define target capabilities, data specs, rubrics and acceptance metrics.
Expert Vetting
Recruited by discipline, admitted by exam, continuously rated and culled.
Human-AI Production
AI drafts, experts revise — humans make the critical calls.
Multi-tier Review
Cross-agreement checks, senior spot checks, independent answer verification.
Auto Checks
Detect AI-written content, eval contamination, duplication and templated output.
Ablations
Small-model training runs to verify the data lifts metrics.
Compliant Delivery
Chain of license, redaction, cross-border assessment, NDAs.
For model teams at every stage
From frontier labs to in-house enterprise teams, model teams at different stages need different data mixes.
| Customer | Common services | Typical goal |
|---|---|---|
| Frontier labs | RL environments · Verifiable hard problems · Preference data · Red teaming | Push the ceiling on reasoning and agentic capability |
| Open-source / mid-size teams | Pre-training corpora · SFT · Preference data | Close the gap with top labs on a limited budget |
| Vertical AI companies | Domain corpora · Domain expert data · Custom evals | Professional-grade depth in finance, healthcare, law and industry |
| Agent startups | Business workflow environments · Tool-use environments · Trajectory data | Agents that reliably get real business work done |
| Enterprise in-house teams | Private data cleaning · Redaction · Fine-tuning data | Turn internal data into trainable assets |
Four engagement models, mix and match
From one-off custom builds to long-term subscriptions, matched to your training cadence and budget structure.
Custom Projects
Data built from scratch to your specs, delivered exclusively to you.
Per item / per hourOff-the-Shelf Datasets
Ready-to-use corpus packages, hard-problem sets and eval sets.
Non-exclusive licenseRL Environment Subscriptions
Environments licensed annually, with ongoing new tasks and fixes.
Annual subscriptionExpert Staffing
You bring the tools and process; we bring vetted experts.
Hourly rate + markupHow It Works
Scoping Call
Pin down target capabilities, data types, scale and acceptance criteria.
Sample Delivery
A small sample batch first, so we align on what quality means.
Paid Pilot
Mid-scale production with ablation or eval results.
Delivery at Scale
Monthly deliveries, with ongoing tracking of model metrics and course corrections.
Talk to us about your data needs
Tell us your target capabilities and current bottlenecks. We'll come back within a week with a data plan and sample schedule.