A technical and operational argument for small, selective RLVR programs over broad throughput-first data factories.
RLVR quality degrades when programs optimize for volume over observability. Generic pipelines can ship lots of trajectories, but often with weak verification and poor domain fidelity.
Boutique teams cap concurrent clients and pick domains where they can model real constraints. This improves reward signal quality and reduces iteration waste.
When senior builders stay involved from environment design through evaluator tuning, bugs are located earlier and corrected with fewer cycles.
Industry experts define edge cases, realistic constraints, and acceptable tradeoffs that synthetic generation misses. Their input prevents reward overfitting to toy proxies.
Scale without deterministic replay, traceability, and strict verification just scales confusion. Reliability infrastructure should come before aggressive expansion.
For frontier teams, the winning pattern is clear: smaller surface area, stronger instrumentation, and higher-conviction domains.
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