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Data-centric scaling of RLVR has focused on collecting or generating verifiable training problems. However, corpus size alone does not determine whether the dataset provides a useful learning signal for a target policy.
Several recent pipelines build RLVR training data by masking a span of real corpus text and asking a language model to invent plausible wrong answers around it. The correct option is therefore genuine human prose; every distractor is synthetic.
AI agents don't get better by reading more of the internet — they get better by practicing inside simulated worlds built for exactly that purpose. This deep dive explains how modern agents actually learn: why human training data hit a wall, how the reinforcement learning loop works, the…
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