Long-tail imbalance is endemic to multi-label learning: a few head labels dominate the gradient signal, while the many rare labels that matter in practice are silently ignored. We tackle this problem by casting the task as a cooperative potential game. In our Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL) framework, the label space is split among several cooperating players that share a global accuracy payoff yet earn additional curiosity rewards that rise with label rarity and inter-player disagreement. These curiosity bonuses inject gradient on under-represented tags without hand-tuned class weights. We prove that gradient best-response updates ascend a differentiable potential and converge to tail-aware stationary points that tighten a lower bound on the expected Rare-F1. Extensive experiments on conventional benchmarks and three extreme-scale datasets show consistent state-of-the-art gains, delivering up to +4.3% Rare-F1 and +1.6% P@3 over the strongest baselines, while ablations reveal emergent division of labour and faster consensus on rare classes. CD-GTMLL thus offers a principled, scalable route to long-tail robustness in multi-label prediction.

翻译：长尾不平衡是多标签学习中的普遍问题：少数头部标签主导梯度信号，而实践中重要的众多稀有标签则被悄然忽视。我们通过将该任务建模为合作势博弈来解决此问题。在我们提出的好奇心驱动的博弈论多标签学习（CD-GTMLL）框架中，标签空间被分配给若干合作参与者，这些参与者共享全局准确度收益，同时获得随标签稀有性和参与者间分歧程度增加的好奇心奖励。这些好奇心奖励为代表性不足的标签注入了梯度，而无需手动调整类别权重。我们证明梯度最优响应更新可提升可微势函数，并收敛到关注尾部标签的稳定点，从而收紧期望稀有标签F1分数的下界。在常规基准测试和三个超大规模数据集上的大量实验显示出稳定且领先的性能提升，相较于最强基线模型，稀有标签F1分数最高提升+4.3%，P@3指标最高提升+1.6%，而消融实验揭示了任务分工的自发涌现以及对稀有类别更快的共识形成。因此，CD-GTMLL为多标签预测中的长尾鲁棒性提供了一条原理清晰、可扩展的路径。