OpenAI describes o1 as trained with large-scale reinforcement learning. DeepSeek-R1 showed that RL can elicit strong reasoning behavior, especially where tasks are verifiable.
Hong Kong AI data and solution partner
RL data and evaluation systems for model builders.
VectraSync helps AI teams turn expert talent, verifiable tasks, agent workflows, and regional data into procurement-ready post-training datasets.
We preserve our AI consulting and developer tooling work, with a new focus on RLHF, RLVR, expert feedback, agent evaluation, and Asia-context data delivery.
Data flywheel
Tasks, verifiers, expert feedback
Delivery focus
Auditable datasets and evals
Hong Kong-based
Asia talent access with international delivery posture
Evaluation-first
Verifier, rubric, and benchmark design before scale
Procurement-ready
Clear scope, provenance, QA, and handoff artifacts
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Why post-training data is becoming a procurement category
Reinforcement learning does not remove data work. It shifts demand toward tasks, validation, expert judgment, and real workflow environments.
Stanford HAI reports very large US private AI investment and a dense AI infrastructure base, while buyers move faster than internal data teams can often support.
MacroPolo's AI talent tracker shows China as a major source of top AI researchers while US institutions remain the dominant destination.
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RL data products we can help deliver
The opportunity is not cheap labeling. It is high-quality, high-verification data that can survive model training, evaluation, and procurement review.
RLHF preference data
Answer pairs, rankings, rubrics, critique, and reviewer guidance for reward modeling, alignment, and usefulness tuning.
RLVR task sets
Code, math, tool-use, STEM, and structured tasks with ground truth, unit tests, checkers, and automatic verification harnesses.
Expert feedback data
Domain review workflows for software engineering, finance, legal, research, multilingual content, and complex reasoning cases.
Agent environments
Browser, IDE, mobile, GUI, and business workflow tasks with trajectories, replay artifacts, and pass/fail evaluation criteria.
Benchmarks and evals
Private benchmark construction, contamination controls, golden sets, scorecards, QA sampling, and regression evaluation plans.
Safety and red-team data
Adversarial prompts, policy edge cases, multilingual safety scenarios, abuse pattern discovery, and escalation rubrics.
03
Who this helps
Specialist datasets for post-training and evaluation.
We help scope verifiable tasks, expert review workflows, and private evaluation assets that are hard to build from general web data.
Supplier capacity with QA and audit artifacts.
We can package datasets with schemas, acceptance criteria, provenance notes, reviewer instructions, and sampling plans.
Agent workflow evaluation before deployment.
We translate real workflows into tasks, expected outputs, failure modes, and test harnesses for model or agent selection.
Chinese, bilingual, cross-border, and regional task coverage.
We focus on non-sensitive, consented, and reviewable data work where Asia-based expertise improves coverage and realism.
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Map a data pilot in one minute.
Pick a task type, validation style, and expertise level. The planner creates a pilot shape you can send with the contact form.
Recommended pilot
Verifier-first RLVR pilot
Send this brief
Start with a narrow task family, define objective pass/fail checks, then deliver seed tasks with tests and reviewer notes.
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Delivery model: verifier first, then scale.
We keep data work measurable before expanding volume.
Scope
Define buyer goal, task family, model behavior to improve, data sensitivity, and acceptance criteria.
Design
Create schemas, rubrics, verifier logic, reviewer instructions, and sample QA plans.
Pilot
Deliver a small but realistic batch with examples, edge cases, artifacts, and issue logs.
Scale
Expand with calibrated reviewers, automated checks, gold samples, and recurring delivery reports.
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Risk-aware from the start
Non-sensitive first. We avoid regulated, defense, medical, and high-risk data unless governance is explicit and appropriate.
Provenance and auditability. Deliverables should include sources, reviewer instructions, QA notes, and acceptance evidence.
Asia talent advantage. Hong Kong positioning lets us coordinate bilingual, Chinese, and regional expertise while keeping an international contracting posture.
Evaluation before volume. We would rather prove task quality and verifier quality before increasing annotation throughput.
Existing AI services remain. We still support AI consulting, developer tools, workflow automation, and model integration when clients need implementation help.
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Read the expanded market thesis and service model
Evidence we build from
These sources inform the market thesis without turning the homepage into a research report.
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Have a dataset, benchmark, or AI workflow to build?
Tell us the task domain, validation method, and target buyer context. We can help shape a pilot dataset, evaluation benchmark, or practical AI implementation.