Reward tooling
Bring human judgment into the discussion of what to optimize, what to measure, and what a favorable assessment really means.
Datlore’s platform brings reward-model tooling together with vetted domain experts and task environments. That connection matters because feedback does not explain itself. A comparison says which response was preferred under certain conditions; it does not automatically establish a universal measure of quality.
Useful reward design starts by asking why a judgment was made. Was one response more accurate, more complete, better suited to the user, or simply easier to read? If those properties move together in every example, a signal may learn an incidental pattern rather than the intended distinction.
The sequence below offers a conceptual way to reason about a program. It does not describe a particular algorithm, model architecture, integration, or guaranteed result. Specific requirements belong in a scoping conversation with the team.
Reading a judgment
The comparative view
A preference task can be a practical way to compare outputs that satisfy a brief to different degrees. Its interpretation depends on the options being compared. Two poor responses can still produce a clear preference; the favored response should not automatically be treated as an acceptable standard.
The dimensional view
A rubric separates dimensions such as factual grounding, constraint adherence, and completeness. This can reveal why the overall choice was difficult. A response might be factually stronger but omit a required qualification. The team needs a reasoned way to weigh that tradeoff rather than an unexplained average.
A reward signal is a proxy for an objective. Inspect where the two can diverge.
Use examples that break superficial correlations. If longer answers often win, compare a concise, correct response with a verbose response that includes an error. If confident language is common in favored outputs, test whether calibrated uncertainty is still recognized when the evidence is incomplete.
Keep some evaluation material separate from the feedback used to shape the signal. Review performance across task types, difficulty levels, and error categories rather than only a single aggregate. A stable average can conceal a serious weakness in a small but important group of tasks.
For episodes, distinguish the quality of the decision from the eventual outcome. A lucky result should not erase an avoidable constraint violation. Conversely, an uncertain environment may produce an unfavorable result even when the available evidence supported the chosen action.
Reward work is not complete when feedback has been converted into a numeric form. Interpretation remains necessary: what did the examples cover, what did they leave out, and which assumptions were baked into the review? Domain experts can help identify meaningful counterexamples and assess whether an apparent improvement reflects the desired capability.
Bring a target behavior, an example comparison, and the evaluation questions you have not yet resolved. A concrete case makes it possible to discuss how expert data, environments, and reward tooling could fit together without assuming a fixed approach.
For a deeper treatment of ambiguous preferences and evaluation, read From preferences to reward signals. For the collection side of the process, explore expert-data planning.
Discuss your learning signal