The platform / A connected learning loop
Human expertise.
Useful learning signals.
Datlore is the expert-data and RL platform for training frontier AI: vetted domain experts, task environments, and reward-model tooling in one place.
The work between a question and a signal.
AI labs use Datlore to source high-quality human feedback and reinforcement-learning episodes at scale.
A model response can be fluent and still miss the point. Understanding that gap often requires someone who knows the domain, understands the task, and can explain which choices matter. Datlore connects that human judgment with the environments and reward tooling used to develop learning signals.
The starting point is a research objective, rather than a volume of labels. What behavior should a model demonstrate? Under which conditions? What evidence would persuade a knowledgeable reviewer that its decision was sound? Those questions help a team scope the expert contribution before deciding how to collect it.
The overview below describes a plausible program structure. Specific task formats, review arrangements, and program requirements should be discussed with the team; the examples are planning guidance rather than a catalog of guaranteed features.
Expertise gives the task context.
Define the knowledge and practical judgment needed for the work. A software review might require experience diagnosing unfamiliar code; a scientific assessment might depend on knowing which assumptions make a conclusion valid. A broad domain label alone rarely communicates that distinction.
Useful scoping describes what experts will assess, what they may reference, and where they should flag uncertainty. That turns expertise from a general qualification into a task-specific contribution.
Define the expertiseTasks make the objective concrete.
A task brief translates an aspiration such as “better reasoning” into an observable decision. It states the available context, allowed actions, constraints, and expected output. The environment supplies the situation in which those decisions can unfold.
For an episode, the sequence matters as much as the final result. Reviewing what was observed and chosen at each step can reveal whether success followed from sound judgment or an accidental shortcut.
Walk through an episodeFeedback makes judgment inspectable.
Comparisons, corrections, rubric assessments, and explanations can expose different aspects of model behavior. Choose a format because it answers the research question. A preference can establish which response a reviewer favors; a rationale can show whether the preference rests on the intended criteria.
Good review also preserves disagreement. Two experts may identify a missing instruction rather than a clearly better answer. That is useful information for improving a task instead of forcing false agreement.
Plan expert dataReward needs interpretation.
Reward-model tooling connects feedback to the question of what behavior a training signal encourages. A concise answer may win a comparison because it is accurate, because it is easier to read, or both. Those explanations imply different lessons for the model.
Keep the original objective visible when evaluating a signal. Examine counterexamples, review held-out tasks, and consider whether a model can receive a favorable assessment without doing the intended work.
Explore reward toolingPutting the parts together
Scope the program before scaling the work.
Consider an illustrative program studying evidence-based planning. The team provides a bounded scenario, several possible actions, and constraints the model must respect. Experts assess the resulting sequence, identify unsupported assumptions, and explain whether the final plan follows from the evidence.
A small initial review can reveal ambiguous instructions, hidden dependencies, or an output format that does not capture important reasoning. Resolve those issues before expanding the task set. More examples cannot compensate for a criterion that consistently rewards the wrong behavior.
Keep changes understandable.
A connected program also needs a clear account of what changed between iterations. A revised brief can alter the meaning of a preference; a new action boundary can change episode difficulty. Preserve these distinctions when comparing results. Otherwise, a shift in feedback may be attributed to the model when it actually reflects a different task.
Ask which observations would lead the team to revise the program. A cluster of unexplained disagreements may call for clearer instructions. Repeated invalid episodes may suggest a missing constraint. Treat these findings as evidence about the collection process as well as about model behavior.
Bring an example task, a description of the target capability, and an initial view of evaluation to a conversation with Datlore. Together, those materials make it easier to discuss the relationship between expert contributions, episodes, and reward.
Discuss your research