About Datlore
Progress begins
with people who know.
Datlore is the expert-data and RL platform for training frontier AI: vetted domain experts, task environments, and reward-model tooling in one place.
Human judgment.
Model progress.
AI labs use Datlore to source high-quality human feedback and reinforcement-learning episodes at scale. The platform connects the people who can assess difficult work with the tasks and tooling that give their assessments a role in training.
That positioning starts with a practical observation: a model’s output can be easy to read and hard to judge. A response may appear complete while relying on an invalid assumption. A decision may reach a favorable outcome without respecting the task. Expertise helps identify those differences and explain why they matter.
The purpose of bringing experts, environments, and reward tooling together is to keep those explanations connected to the work. Expertise alone does not define the task; a task alone does not establish a useful judgment; and a reward signal alone does not prove that the intended behavior is being learned.
The connection matters
as much as the parts.
Consider a research team studying whether a model can reason carefully from evidence. The team needs tasks that reveal that capability, reviewers who understand the evidence, and feedback that distinguishes justified conclusions from plausible guesses. If any of those elements point toward a different objective, the resulting signal can be difficult to interpret.
A coherent program makes the relationship explicit. The brief states what the model should do. The environment establishes what it can observe and choose. Experts assess the relevant behavior. Reward work asks what a favorable judgment actually encourages. Evaluation revisits whether that signal remains useful outside the examples that shaped it.
This is a way to think about program design, not a claim that every project follows the same sequence. Some questions concern a single response; others concern decisions across an episode. The appropriate scope depends on the research objective, the evidence, and the boundaries of the work.
Principles for a useful learning loop.
Make the objective concrete.
Broad aspirations need observable tasks. Describe which decision matters, what conditions surround it, and what would count as evidence of good judgment. Concrete examples often reveal missing assumptions faster than an abstract specification.
Respect the depth of expertise.
Domain judgment is more than recognizing terminology. It includes knowing where a method applies, which evidence is decisive, and when a conclusion is uncertain. Define the expertise the task actually needs and leave space for reviewers to explain its limits.
Keep signals inspectable.
A score is easier to use when the team understands the criterion behind it. Preserve context, examine disagreement, and look for counterexamples. Interpretability of the feedback helps teams decide what to revise and what additional evidence to collect.
Let uncertainty inform the work.
An ambiguous task, incomplete observation, or unresolved tradeoff can teach a team something useful. Treat those cases as reasons to investigate rather than automatically forcing a confident label. The boundary of a judgment can be as informative as the judgment itself.
Two perspectives.
A shared question.
For AI labs, the question is how expert contributions can help study and train a particular capability. Useful discussion begins with a task, an intended behavior, and the evaluation question the current process leaves open. The guide for AI labs offers a starting point.
For domain experts, the question is how to make their knowledge useful to that research. Reviewing an argument, identifying a failure, or explaining a tradeoff can turn tacit understanding into feedback another person can inspect. The expert contribution guide describes illustrative ways to contribute and expectations for careful work.
The public site explains Datlore’s positioning and provides general research-planning guidance. Examples are not a substitute for a discussion of a specific program, and they do not establish commercial terms or service commitments. Bring your context to the conversation so the requirements can be addressed directly.
Explore the platform overview, read the learning-loop articles, or start with a short description of the problem you are working on.
Talk to our team