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Composing aesthetic preference with explicit rubric rewards unlocks substantial gains in text-to-image generation, outperforming existing open-source models by a significant margin.
A novel kernel function circumvents gradient starvation in off-policy RLVR, enabling learning from previously discarded positive trajectories.
BAS-OPD is proposed, a budget-aware selective OPD framework that allocates teacher supervision under limited query budgets that achieves strong performance while substantially reducing teacher supervision costs, highlighting the effectiveness of selective OPD under constrained budgets.