The framework accommodates Dynamic Sampling Policy Optimization, which screens out 'unproductive' prompts generating identical outcomes. ProRL AGENT uses parallel replenishment to sustain peak efficiency, canceling surplus active tasks once sufficient productive prompts are collected.
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