01 / Insights
What makes expert feedback useful?
Specific evidence, clear criteria, and explanations that help distinguish a correct answer from a convincing one.
Read the articleThe next frontier
begins with human expertise.
HUMAN JUDGMENT.
MODEL PROGRESS.
Datlore is the expert-data and reinforcement-learning platform for training frontier AI: vetted domain experts, task environments, and reward-model tooling in one place.
Expert dataHuman feedbackRL episodesReward tooling
01
Expertise that goes
beyond annotation.
AI labs use Datlore to source high-quality human feedback and reinforcement-learning episodes at scale, grounded in the judgment of vetted domain experts.
Useful judgment depends on context. Define what an expert should assess, what evidence they can use, and what makes a response worth learning from.
- Science
- Software
- Mathematics
- Professional domains
From expert judgment to learning signal.
01Define the task
Turn research goals into clear tasks, boundaries, and review criteria that domain experts can apply.
02Capture expert feedback
Make reasoning visible through comparisons, explanations, corrections, and carefully scoped interactions.
03Shape the reward
Consider how feedback informs reward signals, alongside evaluation of what the signal actually encourages.
A training ground
for better decisions.
For AI labs
Build the signals
your models need.
Connect expert contributions to the research question. Start with the behavior you want to study, then scope the tasks and feedback that could reveal it.
Discuss your programFor domain experts
Bring depth to the next
generation of AI.
Apply your knowledge to challenging problems. Explore how subject expertise can contribute to task design, evaluation, and useful human feedback.
Explore expert contributionsInside the learning loop.
02 / Practice
Designing tasks for reinforcement learning
How task boundaries, available actions, and review criteria shape the meaning of an episode.
Read the article03 / Perspective
From preferences to reward signals
Turning expert comparisons into training information while keeping the underlying objective in view.
Read the articleFrequently asked questions.
What does Datlore bring together?
Datlore brings vetted domain experts, task environments, and reward-model tooling into one platform so AI labs can source high-quality human feedback and reinforcement-learning episodes at scale. Explore the platform overview to understand how these parts connect.
Who is Datlore built for?
Datlore serves AI labs training frontier models and domain experts contributing informed judgment. A research discussion can begin with the capability being studied, the task context, and the kind of feedback the team needs.
What kinds of expertise matter?
The relevant expertise depends on the task. Science, software, mathematics, and professional reasoning are illustrative examples. Define the knowledge, practical experience, and boundaries required for your particular research question; these examples are not a confirmed roster of available specialists.
How should a team prepare for a conversation?
Bring a short description of your research objective, an example task, and your initial review criteria. Explain whether you need individual judgments, comparisons, or feedback on decisions across an episode. Avoid sending confidential datasets in an initial inquiry.
How can domain experts express interest?
Read the expert contribution guide and use its interest form to describe your area of expertise and the kinds of problems you work on. Expressing interest does not guarantee a particular project or engagement.
Put human expertise
at the heart of progress.
Useful learning signals begin with thoughtful human judgment. Let’s explore how Datlore could support your next research program.
Talk to our team