Resources / Ideas into practice
Useful training information begins with careful questions. Explore the choices behind expert feedback, task design, and reward signals.
01 / Insights / Featured reading
Expertise alone does not guarantee a useful review. The brief, available evidence, feedback format, and explanation all shape what the resulting judgment can tell a research team.
Start here if you are defining review criteria or interpreting disagreement. Work through an evidence-based example, then use a checklist to inspect an initial sample.
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Practice / Task design
A task establishes which behavior can be observed. Boundaries, observations, actions, and termination conditions determine whether an episode reveals the capability you intended to study.
Read the articleRead this when moving from an abstract research goal to an episode. Follow an illustrative planning task through its design choices, then examine shortcuts, reviewability, and evaluation separation.
Perspective / Reward
A preference identifies a choice in context. Converting that choice into a signal requires attention to criteria, ambiguity, and the difference between a proxy and the underlying objective.
Read the articleUse this to frame questions about what favorable assessments encourage. It covers conflicting criteria, superficial correlations, counterexamples, and evaluation that goes beyond an overall score.
The three articles follow connected parts of a research process, but each can be read independently. If you already have a task and need better judgments, begin with expert feedback. If your objective is broad and the task is still taking shape, begin with task design. If feedback is available but its meaning as a reward is unclear, begin with preferences and signals.
The articles also help distinguish three kinds of revision. A judgment that lacks evidence may require a clearer review brief. An episode that never exposes the intended decision may require a different task. A favorable score that tracks presentation rather than substance may require counterexamples and a revised evaluation. Naming the source of the problem makes the next experiment more purposeful.
Keep one representative example beside you as you read. Ask how the ideas would change its instructions, its review criteria, or the evidence you would retain. A specific case makes it easier to distinguish advice that applies to your program from questions that require a different design.
These are general research-planning perspectives with illustrative examples. They do not document a particular algorithm, guarantee an outcome, or establish the specifications of a Datlore engagement. Use them to sharpen a conversation about your own requirements.
Datlore brings vetted domain experts, task environments, and reward-model tooling together for AI labs training frontier AI. See the platform overview for how those parts connect, or the AI-lab guide for a program-scoping starting point.
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