Task environments
Give better decisions
a place to unfold.
An environment turns an abstract capability into a situation with context, choices, and consequences that experts can examine.
More than a final answer.
The episode below is a conceptual walkthrough, not a live interface or a promise of a particular implementation.
Datlore brings task environments together with vetted domain experts and reward-model tooling. For AI labs sourcing reinforcement-learning episodes, the environment matters because it defines what a model can observe, what it can do, and what counts as progress.
A final result rarely tells the whole story. A model might reach a valid answer after ignoring a required constraint, or make a sensible decision with the evidence available and still encounter an unfavorable outcome. Reviewing the sequence helps distinguish decision quality from outcome alone.
Clear task boundaries make this possible. State where the episode begins, which information is provided, which actions are permitted, and when the episode ends. The review criteria should refer to behavior that is actually visible within those boundaries.
Inside an episode
One brief.
A sequence
of judgments.
Follow an illustrative evidence-based planning task from its starting conditions to expert review.
Read the task-design guideStarting conditions
Set the objective.
A model is asked to propose a plan under a fixed set of resource constraints. The brief supplies several candidate actions and the evidence relevant to each. Success means presenting a feasible plan with a rationale that respects the provided constraints.
Before the episode starts, a reviewer should be able to distinguish a hard constraint from a preference. If the brief does not make that distinction, inconsistent judgments may reflect task ambiguity rather than model behavior.
What the model knows
Define observations.
The initial observation includes the goal, available resources, and relevant uncertainty. Further information might be available only through a permitted action. Record what the model knew at the moment it made each choice.
Do not assess a decision using information that appears only later. A reviewer can identify a poor outcome while recognizing that the earlier decision was reasonable given its evidence.
What the model can do
Bound the actions.
In this example, the model may inspect a candidate, request a clarification from a fixed source, revise its plan, or submit a final proposal. Each action has a stated meaning. Actions that change the world outside the task are outside this illustrative boundary.
A bounded action space helps the team interpret the sequence. It also makes it easier to identify shortcuts, repeated unproductive steps, and unsupported assumptions that a final-answer review would overlook.
The learning opportunity
Review the episode.
An expert examines whether the model used evidence appropriately, respected the constraints, and responded sensibly to uncertainty. A rationale points to the relevant observation or action rather than simply labeling the episode successful.
The review can separate outcome, process, and avoidable error. That separation creates more useful material for discussing a reward signal than a single undifferentiated score.
Design for interpretable review.
Episode records should preserve enough context for the review question. In a debugging example, that could mean the issue description, code state, chosen inspection steps, and the evidence supporting the final change. In a reasoning example, it could mean assumptions, intermediate claims, and the point at which a constraint was applied.
These are examples of task structure, not statements about supported tools or integrations. Discuss the required environment, data boundaries, and review materials before assuming a particular workflow can be delivered. What matters first is a coherent relationship between the research objective and the observable episode.
Consider termination carefully. An episode might end at a final submission, a defined limit, or a condition that makes further progress impossible. Unfinished and invalid episodes can carry information, but they should not be silently treated as ordinary completed examples.
Questions to resolve before scale.
- Can the intended capability be observed in this task?
- What information can the model access at each step?
- Which shortcuts would satisfy the score without meeting the objective?
- How will reviewers distinguish uncertainty, failure, and ambiguity?
- What examples will remain separate for evaluation?
Start with a small collection of representative episodes and review the boundaries before expanding. Read Designing tasks for reinforcement learning for a practical design sequence, or discuss your task context with the team.
Discuss a task environment