Turning Industry Expertise into RLVR Task Data

Turning Industry Expertise into RLVR Task Data

Great RLVR data is rarely scraped; it is structured from expert practice. The goal is to encode how professionals actually make decisions under uncertainty.

Step 1: Interview for Decision Points

Ask experts where mistakes are costly, where tradeoffs are unavoidable, and what evidence is required to move forward.

Step 2: Convert Narratives into Task Graphs

Map each workflow into nodes, transitions, and failure conditions. Each node should be observable and scoreable.

Step 3: Define Verifier Contracts

For every task stage, specify what must be true: numerical tolerance, source requirements, policy checks, and tool usage constraints.

Step 4: Build Regression Packs

Once tasks are live, freeze representative passes and fails into regression packs so future policy changes can be tested against known behavior.

This process turns domain expertise into reusable RLVR infrastructure instead of one-off prompt artifacts.